Code for Computer Vision Algorithms

The first place to look for basic code to implement basic computer vision algorithms is the OpenCV Library from Intel. Many research groups build on top of the OpenCV code base. The avaliability of code for specific techniques or applications is harder to predict, but some implementations are made available by the authors and others are available from commercial vendors. The following pointers are organized somewhat by what the code does, with other links by what language the code is written in. If you follow the link for the code reference you may find links to the related papers in the Computer Vision Bibliography. Additionally, following the link to the Author often leads to a site with more code. A number of lists of code for sub-areas (e.g. OCR) have been created by researchers in the past, but too often these are no longer maintained. These lists are included in the my listing along with many of the individual entries in those lists. Current research and applications are highlighted in various general and topical Computer Vision and Image Processing conferences, especially applications workshops. As always, the links may not work for all time -- I have no control over the actions of others who move or delete files, but often broken links can provide a starting place to find the source.

Computer Vision resources include:

For more information on the topics, contact information, etc. see the annotated Computer Vision Bibliography or the Complete Conference Listing for Computer Vision and Image Analysis

Detailed Entries for Code

Assens, M.[Marc], Giro-i-Nieto, X.[Xavier], McGuinness, K.[Kevin], O'Connor, N.E.[Noel E.],
Scanpath and saliency prediction on 360 degree images,
SP:IC(69), 2018, pp. 8-14.
Elsevier DOI

Earlier:
SaltiNet: Scan-Path Prediction on 360 Degree Images Using Saliency Volumes,
Egocentric17(2331-2338)
IEEE DOI
Code, Saliency.
WWW Link. Deep learning, Machine learning, Saliency, Scanpath, Visual attention. Biological system modeling, Computational modeling, Observers, Predictive models, Training, Visualization

Chen, Q.[Qiang], Wu, Q.[Qiman], Wang, J.[Jian], Hu, Q.H.[Qing-Hao], Hu, T.[Tao], Ding, E.[Errui], Cheng, J.[Jian], Wang, J.D.[Jing-Dong],
MixFormer: Mixing Features across Windows and Dimensions,
CVPR22(5239-5249)
IEEE DOI
Code, Attention.
WWW Link. Couplings, Codes, Convolution, Bidirectional control, Transformers, Pattern recognition, Recognition: detection, categorization, retrieval

Long, F.C.[Fu-Chen], Qiu, Z.F.[Zhao-Fan], Pan, Y.W.[Ying-Wei], Yao, T.[Ting], Luo, J.B.[Jie-Bo], Mei, T.[Tao],
Stand-Alone Inter-Frame Attention in Video Models,
CVPR22(3182-3191)
IEEE DOI
Code, Attention.
WWW Link. Convolutional codes, Weight measurement, Deep learning, Solid modeling, Computational modeling, Transformers, Video analysis and understanding

Cao, S.Y.[Si-Yuan], Hu, J.X.[Jian-Xin], Sheng, Z.[Zehua], Shen, H.L.[Hui-Liang],
Iterative Deep Homography Estimation,
CVPR22(1869-1878)
IEEE DOI
Code, Homography.
WWW Link. Deep learning, Codes, Estimation, Computer architecture, Pattern recognition, Iterative methods, Low-level vision, Deep learning architectures and techniques

Bouguet, J.Y.,
Matlab Camera Calibration Toolbox,
TRCalTech, 2015.
HTML Version. Code, Camera Calibration.

Marquardt, D.,
An Algorithm for Least-Squares Estimation of Nonlinear Parameters,
SIAM_JAM(11), 1963, pp. 431-441. Levenberg-Marquardt is a popular alternative to the Gauss-Newton method of finding the minimum of a function F(x) that is a sum of squares of nonlinear functions.
HTML Version. Code, Levenberg-Marquardt.

Zach, C.[Christopher],
Simple Sparse Bundle Adjustment (SSBA),
2008 Code, Bundle Adjustment.
HTML Version.

Lourakis, M.I.A.[Manolis I. A.],
Sparse Non-linear Least Squares Optimization for Geometric Vision,
ECCV10(II: 43-56).
Springer DOI
Code, Optimization.
WWW Link. Sparse.
See also Levenberg-Marquardt nonlinear least squares algorithms in C/C++.

Lourakis, M.I.A.[Manolis I.A.], and Argyros, A.A.,
The Design and Implementation of a Generic Sparse Bundle Adjustment Software Package Based on the Levenberg-Marquardt Algorithm,
TR340, Institute of Computer Science - FORTH, Heraklion, Crete, Greece, August, 2004. (updated August 2009).
WWW Link. Code, Bundle Adjustment. Publicly available (GPL) C/C++ software package for generic sparse bundle adjustment based on the Levenberg-Marquardt algorithm.

Lourakis, M.I.A.[Manolis I.A.],
Levenberg-Marquardt nonlinear least squares algorithms in C/C++,
OnlineApril 2009.
WWW Link. Code, Levenberg-Marquardt. Publicly available (GPL) C/C++ software package for Levenberg-Marquardt algorithm.
See also Sparse Non-linear Least Squares Optimization for Geometric Vision.

Marí, R.[Roger], de Franchis, C.[Carlo], Meinhardt-Llopis, E.[Enric], Anger, J.[Jérémy], Facciolo, G.[Gabriele],
A Generic Bundle Adjustment Methodology for Indirect RPC Model Refinement of Satellite Imagery,
IPOL(11), 2021, pp. 344-373.
DOI Link
Code, Bundle Adjustment.

Pierrot Deseilligny, M., Clery, I.,
Apero, An Open Source Bundle Adjusment Software For Automatic Calibration and Orientation of Set of Images,
3DARCH11(xx-yy).
PDF File.
Code, Bundle Adjustment.

El-Sheimy, N.[Naser],
Addingham Bundle Adjustment,
Online2007. University Calgary.
HTML Version. Code, Bundle Adjustment.

Julià, L.F.[Laura F.], Monasse, P.[Pascal], Pierrot-Deseilligny, M.[Marc],
The Orthographic Projection Model for Pose Calibration of Long Focal Images,
IPOL(9), 2019, pp. 231-250.
DOI Link
Code, Pose Calibration. Matlab implementation. As in telecentric image.
See also Paraperspective Factorization Method for Shape and Motion Recovery, A.
See also Calibration of long focal length cameras in close range photogrammetry.

HySCaS: Hybrid Stereoscopic Calibration Software,
Online2011.
WWW Link. Code, Calibration.

Tsai, R.Y.,
A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses,
RA(3), No. 4, 1987, pp. 323-344. Code, Camera Calibration. Code:
HTML Version.
Earlier:
An Efficient and Accurate Camera Calibration Technique for 3-D Machine Vision,
CVPR86(364-374). Lens Distortion. This was the "best" paper at the conference. Various techniques for calibration and error analysis. Use 60 Control points to derive camera parameters (position, orientation, focal length, radial lens distortion, and image scanning parameters). Compute intrinsic and extrinsic parameters using a planar or non-planar test pattern. Criticized for not using rigorous least-squares method.

Alvarez, L.[Luis], Gomez, L.[Luis], Sendra, J.R.[J. Rafael],
Algebraic Lens Distortion Model Estimation,
IPOL(2010), No. 1, 2010, pp. xx-yy.
DOI Link
Code, Lens Distortion.

Alemán-Flores, M.[Miguel], Alvarez, L.[Luis], Gomez, L.[Luis], Santana-Cedrés, D.[Daniel],
Automatic Lens Distortion Correction Using One-Parameter Division Models,
IPOL(2014), No. 2014, pp. 327-343.
DOI Link
Code, Distortion Correction.
See also Wide-Angle Lens Distortion Correction Using Division Models.

Santana-Cedrés, D.[Daniel], Gómez, L.[Luis], Alemán-Flores, M.[Miguel], Salgado, A.[Agustín], Esclarín, J.[Julio], Mazorra, L.[Luis], Álvarez, L.[Luis],
An Iterative Optimization Algorithm for Lens Distortion Correction Using Two-Parameter Models,
IPOL(6), 2016, pp. 326-365.
DOI Link
Code, Lens Distortion.
See also Invertibility and Estimation of Two-Parameter Polynomial and Division Lens Distortion Models.

Lalonde, J.F.[Jean-François], Narasimhan, S.G.[Srinivasa G.], Efros, A.A.[Alexei A.],
What Do the Sun and the Sky Tell Us About the Camera?,
IJCV(88), No. 1, May 2010, pp. xx-yy.
Springer DOI

Earlier:
What Does the Sky Tell Us about the Camera?,
ECCV08(IV: 354-367).
Springer DOI
WWW Link.

And:
What Do the Sun and Sky Tell Us About the Camera?,
CMU-RI-TR-09-04, January, 2009.
WWW Link.
And:
Camera parameters estimation from hand-labelled sun positions in image sequences,
CMU-RI-TR-08-32, July, 2008.
WWW Link. Code, Illumination. The sky is the main observed illuminant of the outdoor scene. Appearance of the sky can inform on the sun position. Apply to a sequence and determine geolocation (from web cams). Code:
WWW Link.

Mitsunaga, T.[Tomoo], Nayar, S.K.[Shree K.],
Radiometric Self Calibration,
CVPR99(I: 374-380).
IEEE DOI
PDF File. Code, Radiometric Calibration.
WWW Link.

Zhang, Z.Y.[Zheng-You],
A Flexible New Technique for Camera Calibration,
PAMI(22), No. 11, November 2000, pp. 1330-1334.
IEEE DOI

And: MicrosoftMSR-TR-98-71, December 1998.
PS File. Code, Camera Calibration. And for Code:
WWW Link. Planar pattern in at least 2 orientations.

Flightmare,
2020.
WWW Link. Code, Drone Control. Photorealistic, customizable, and easy to use simulator for quadrotors! It is compatible with ROS, Gazebo, OpenAI Gym, and even Oculus #VR headsets. Also a set of reinforcement learning baselines for benchmarking.

Loquercio, A.[Antonio], Kaufmann, E.[Elia], Ranflt, R.[Rene], Mueller, M.[Matthias], Koltun, V.[Vladlen], Scaramuzza, D.[Davide],
Learning High-Speed Flight in the Wild,
Science Robotics2021.
WWW Link. Project page:
HTML Version. Code, dataset page:
WWW Link. Code, Drone Control.

Dawkins, M.[Matthew], Sherrill, L.[Linus], Fieldhouse, K.[Keith], Hoogs, A.[Anthony], Richards, B.[Benjamin], Zhang, D.[David], Prasad, L.[Lakshman], Williams, K.[Kresimir], Lauffenburger, N.[Nathan], Wang, G.A.[Gao-Ang],
An Open-Source Platform for Underwater Image and Video Analytics,
WACV17(898-906)
IEEE DOI
Code, AVU. Detectors, MATLAB, Open source software, Pipelines, Streaming, media

Ultimate SLAM,
2022.
WWW Link. Code, SLAM. Events, frames, and IMU to achieve robust visual SLAM in high speed and high dynamic range scenarios.
See also Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios.

Cioffi, G., Cieslewski, T., Scaramuzza, D.,
Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study,
RALetters(7), 2022.
PDF File.
WWW Link. Code, SLAM.

Schörghuber, M.[Matthias], Steininger, D.[Daniel], Cabon, Y.[Yohann], Humenberger, M.[Martin], Gelautz, M.[Margrit],
SLAMANTIC: Leveraging Semantics to Improve VSLAM in Dynamic Environments,
DeepSLAM19(3759-3768)
IEEE DOI
Code, SLAM.
WWW Link. learning (artificial intelligence), semantic networks, SLAM (robots), pose computation, geometric pipeline, SLAMANTIC, robust vslam

Steux, B., El Hamzaoui, O.,
tinySLAM: A SLAM algorithm in less than 200 lines C-language program,
ICARCV10(1975-1979).
IEEE DOI
Code, SLAM.

Nisar, B.[Barza], Foehn, P.[Philipp], Falanga, D.[Davide], Scaramuzza, D.[Davide],
VIMO: Simultaneous Visual Inertial Model-based Odometry and Force Estimation,
RSS19. (xx-yy).
HTML Version.
Code, Odometry. Extends the capability of a typical optimization-based Visual-Inertial Odometry framework to jointly estimate external forces in addition to the robot state and IMU bias

Kimera,
2019
WWW Link. Code, SLAM.
Kimera is implemented in C++, is ROS-compatible, and runs on CPU. It only uses a camera and an IMU. The code performs real-time visual-inertial SLAM (including robust loop closure detection and outlier removal) and builds a semantically labeled 3D mesh. Kimera is a hybrid creature and includes state-of-the-art algorithms for visual-inertial odometry, robust pose graph optimization, real-time mesh reconstruction, and 3D semantic segmentation.

Liu, L., Li, H., Dai, Y.,
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization,
ICCV19(2570-2579)
IEEE DOI
Code, Localization.
WWW Link. feature extraction, image recognition, image representation, Image databases, image retrieval, learning (artificial intelligence), probability.

Hays, J.H.[James H.], Efros, A.A.[Alexei A.],
IM2GPS: estimating geographic information from a single image,
CVPR08(1-8).
IEEE DOI
WWW Link.
Code, Localization.
HTML Version.

Huang, Z., Xu, Y., Shi, J., Zhou, X., Bao, H., Zhang, G.,
Prior Guided Dropout for Robust Visual Localization in Dynamic Environments,
ICCV19(2791-2800)
IEEE DOI
Code, Localization.
WWW Link. cameras, convolutional neural nets, geometry, graph theory, image motion analysis, object detection, optimisation, Uncertainty

Ghose, S.[Shuvozit], Chowdhury, P.N.[Pinaki Nath], Roy, P.P.[Partha Pratim], Pal, U.[Umapada],
Modeling Extent-of-Texture Information for Ground Terrain Recognition,
ICPR21(4766-4773)
IEEE DOI
Code, Terrain.
WWW Link. Pairwise error probability, Image recognition, Shape, Message passing, Feature extraction, Data mining, Task analysis

Weng, X., Kitani, K.,
Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud,
CVRSUAD19(857-866)
IEEE DOI
Code, Object Detection.
WWW Link. image representation, image sensors, object detection, optical radar, KITTI benchmark, 3D localization, Point Cloud

OpenCV,
IntelSeptember 2009. Code, Image Processing. Code, Computer Vision. Code, Image Processing, C. Code, Open Source.
WWW Link. And the Source Forge reference:
WWW Link. Version 2.0 released in 2009. C and C++ interfaces. Original version was August 2000. The standard open source code for many basic computer vision tasks. Support and development from
See also Willow Garage.

Fiji Image Processing Package,
2016 Code, Image Processing. Code, Computer Vision. Code, Image Processing, Java.
WWW Link. Fiji is an image processing package: a "batteries-included" distribution of ImageJ, bundling a lot of plugins which facilitate scientific image analysis.

ImageJ: Image Processing and Analysis in Java,
2007. Code, Image Processing. Code, Image Processing, Java.
WWW Link. A set of public domain image (US Government code) analysis routines in Java.

ImageJ-Plugins -- Various Plugins for the image manipulation software ImageJ,
2007. Code, Image Processing. Code, Image Processing, Java.
HTML Version.
See also University of Freiburg.

Image Processing Library 98,
2003.
WWW Link. Code, Image Analysis. Code, Image Processing, C++. A platform independent image manipulating C/C++ library.

Recognition And Vision Library,
2003.
WWW Link. Code, Image Analysis. Code, Image Processing, C++. C++ class library together with a range of computer vision, pattern recognition, audio and supporting tools.
See also University of Surrey.

C++ Template Image Processing Library,
2000.
WWW Link. Code, Image Analysis. Code, Image Processing, C++. Code, Open Source. INRIA derived code.

IT++ Mathematical, Signal Processing and Communication Routines,
2007
WWW Link. Code, Signal Processing. Code, Signal Processing, C++.

ImageLib: An Image Processing C++ Class Library,
2000.
WWW Link. Code, Image Analysis. Code, Image Processing, C++. C++ class library providing image processing and related facilities.
See also University of Cape Town.

NeatVision,
2000. Code, Image Analysis.
WWW Link. Code, Image Processing, Java. NeatVision is a free Java based image analysis and software development environment, which provides high level access to a wide range of image processing algorithms. From:
See also Dublin City University, Machine Vision Group.

The Delft Image Processing library,
2000.
WWW Link. Code, Image Analysis. Code, Image Analysis, Matlab. Code, Image Analysis, C. DIPimage is a MATLAB toolbox. DIPlib is a platform independent scientific image processing library written in C. Formerly ImLib3D From:
See also Delft University of Technology.

LibCVD: computer vision library,
2009.
HTML Version. Code, Image Analysis. Code, Image Processing, C++. Portable C++ library providing image processing and related facilities.

PEIPA Computer Vision Software,
Online2004.
HTML Version. Code, Computer Vision. Dataset. Pilot European Image Processing Archive. This lists a number of sources for various alogrithms. They also include pointers to the usual set of image databases.

LTI-Lib,
Online2005.
WWW Link. Code, Image Processing. Code, Image Processing, C++. Code, Open Source. Object oriented library with algorithms and data structures frequently used in image processing and computer vision.
See also Aachen University of Technology.

Mimas,
January, 2006.
WWW Link. Code, Image Processing. Code, Image Processing, C++. Code, Open Source. C++ toolkit, corners, etc.

MediaCybernetics,
2005. Vendor, Software. Code, Image Processing.
WWW Link. A set of Image Analysis products, especially applied to microscope images and scientific applications.

Bioimage Suite,
1998.
WWW Link. Code, Image Processing. Yale group. Biomedical imaging and visualization.

Insight Segmentation and Registration Toolkit (ITK),
2022.
WWW Link. Code, Medical Image Analysis. NIH toolkit for medical processing.

Noesis Vision,
2007.
HTML Version. Code, Image Analysis. Primarily bio, chemistry, but others. Visilog product

Generic Programming for Computer Vision: The VIGRA Computer Vision Library,
OnlineDecember, 2006. Code, Image Processing. Code, Image Processing, C++. C++ implementation, using standard template library.

Torch3vision: Machine Vision Library,
2007
WWW Link. Code, Image Analysis. Derived from the machine learning library:
See also Torch: Machine-Learning Library.

Microsoft Kinect SDK,
2011
WWW Link. Vendor, Image Analysis. Code, Image Analysis. Early version of SDK for noncommercial application development.
See also Microsoft Kinect.

The Mobile Robot Programming Toolkit,
2011 Code, Image Processing. Code, Image Processing, C++.
WWW Link. Localization, Simultaneous Localization and Mapping (SLAM), computer vision and motion planning (obstacle avoidance). From: University of Malaga.
See also University of Malaga.

QCV,
2012 Code, Image Processing. Code, Image Processing, C++.
WWW Link. A computer vision framework based on Qt and OpenCV that provides an interface to display, analyze and run computer vision algorithms.

Walrus Vision Toolbox,
2014 Code, Image Processing.
WWW Link. Vision toolbox for image processing. Device controllers, pattern recognition.

Core Imaging Library (CIL),
2019
WWW Link. Code, Image Analysis. Code, CT Data Analysis. The Core Imaging Library (CIL) is a set of modules for each process involved in the data analysis workflow of the CT datasets.

DgiStreammer,
2021 Code, Image Processing.
WWW Link.
Imaging/video pipelines, including the use of deeplearning blocks such as deepstream, and deploy it anywhere.

Bailey, D.G., Hodgson, R.M.,
VIPS: A Digital Image Processing Algorithm Development Environment,
IVC(6), No. 3, August 1988, pp. 176-184.
Elsevier DOI Code, Image Processing.
WWW Link.
See also University of Southampton.

Kovesi, P.[Peter],
MATLAB and Octave Functions Software for Computer Vision and Image Processing,
Online2007. Code, Computer Vision. Code, Computer Vision, Matlab.
WWW Link.
See also University of Western Australia.

VXL,
Online2004.
WWW Link. Code, Computer Vision. Code, Computer Vision, C++. Code, Open Source. VXL (the Vision-something-Libraries) is a collection of C++ libraries designed for computer vision research and implementation. It was created from TargetJr and the IUE with the aim of making a light, fast and consistent system.

LibTIFF: TIFF Library and Utilities,
Code, Image Processing. Code, TIFF.
WWW Link. The Tag Image File Format (TIFF) pages. The site is now maintained through
See also OSGeo: Open Source Geospatial Foundation.

pbmplus Image File Format Conversion Package,
2001 Code, Image Processing. PBMPlus.
WWW Link. The standard toolkit for command line conversion of image formats. Everything to everything else.

IFS: Image File System,
Code, Image Processing.
HTML Version. Variety of image handling rountines. From NCSU

ImageMagick,
1999. Code, Image Processing.
WWW Link. A software suite to create, edit, and compose bitmap images. Command line and libraries called from your programs.

GNU Image Manipulation Program,
2001 Code, Image Processing.
WWW Link. The GNU based (i.e. free) set of image manipulation programs intended to compete with the more expensive professional programs. Almost, but not quite Photoshop.

Skiljan, I.[Irfan],
IrfanView,
Online1996. Code, Image Processing.
WWW Link. A useful basic image processing tool. Not too fancy, but does a lot of basic tasks. Includes batch mode processing for a list of files.

Rad Video Tools,
1988. Code, Video Processing. A package of tools for game developers, but also includes a package of video tools for conversions and other operations.

Supercomputing Systems: Vision,
2000. Code, Image Processing.
HTML Version. Image processing systems. leanXcam.

Landy, M.S.[Michael S.], Cohen, Y.[Yoav], and Sperling, G.[George],
HIPS: A Unix-Based Image Processing System,
CVGIP(25), No. 3, March 1984, pp. 331-347.
Elsevier DOI Code, Image Processing.
HTML Version.
And:
HIPS: Image Processing Under UNIX. Software and Applications,
BehResMeth(16), No. 2, 1984, pp. 199-216. The system is commercially available.

Groningen Image Processing System, GIPSY,
TR1992.
WWW Link. System: Gipsy. Code, Image Processing. There are 2 systems by the same name. They are different.

Pope, A.R., Lowe, D.G.,
Vista: A Software Environment for Computer Vision Research,
CVPR94(768-772).
IEEE DOI System: Vista. Code, Image Analysis.
HTML Version.

Whittenburg, T.,
Photo-Based 3D Graphics in C++: Compositing, Warping, Morphing, and Other Digital Special Effects,
John Wiley& Sons, 1995, ISBN 0-471-04972-7. Code, Image Processing. Code, Image Processing, C++. Contains a disk with the programs. Buy this book: Photo-Based 3d Graphics in C++: Compositing, Warping, Morphing and Other Digital Special Effects/Book and Disk

Dobie, M.R., Lewis, P.H.,
Data Structures for Image Processing in C,
PRL(12), 1991, pp. 457-466. Code, Image Processing. Code, Image Processing, C.

Skibbe, H.[Henrik], Reisert, M.[Marco],
Spherical Tensor Algebra: A Toolkit for 3D Image Processing,
JMIV(58), No. 3, July 2017, pp. 349-381.
Springer DOI
Code, Tensor Algebra. For processing 3D data.

Jørgensen, J.S., Ametova, E., Burca, G., Fardell, G., Papoutsellis, E., Pasca, E., Thielemans, K., Turner, M., Warr, R., Lionheart, W.R.B., Withers, P.J.,
Core Imaging Library - Part I: a versatile Python framework for tomographic imaging,
Royal(A: 379), No. 2204, August 2021, pp. 20200192.
DOI Link
Code, CT.

Papoutsellis, E.[Evangelos], Ametova, E.[Evelina], Delplancke, C.[Claire], Fardell, G.[Gemma], Jørgensen, J.S.[Jakob S.], Pasca, E.[Edoardo], Turner, M.[Martin], Warr, R.[Ryan], Lionheart, W.R.B.[William R. B.], Withers, P.J.[Philip J.],
Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography,
Royal(A: 379), No. 2204, August 2021, pp. 20200193.
DOI Link
Code, CT.

Peter, J.[Jörg],
Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences,
Royal(A: 379), No. 2204, August 2021, pp. 20200190.
DOI Link
Code, CT.

Peng, G.[Gao], Pang, B.[Bo], Lu, C.[Cewu],
Efficient 3D Video Engine Using Frame Redundancy,
WACV21(3791-3801)
IEEE DOI
WWW Link.
Code, Video Processing. Efficient 3D Video Engine (EVE). Geometry, Solid modeling, Computational modeling, Redundancy, Pipelines, Feature extraction

OpenVidia,
Online2006.
WWW Link. Code, Open Source. Code, GPU. Code, Computer Vision. Opensource computer vision algorithms for computer graphics hardware. Uses OpenGL, Cg, CUDA. Primarily from the Toronto group.
See also University of Toronto.

GPU4Vision,
Online2009.
WWW Link. Code, Computer Vision. Code, GPU. Opensource computer vision algorithms for NVIDIA hardware.
See also Graz University of Technology.

AccelerEyes,
2008
WWW Link. Code, Computer Vision. Code, GPU. Matlab toolbox to generate code for GPUs.

FastCV,
October 2011. Code, Image Processing. Code, Computer Vision. Code, Image Processing, C.
WWW Link. A mobile-optimized library to develop computer vision for the phone. Optimized for Qualcomm processors, but runs on others.

Shirahatti, N.V.[Nikhil V.], Barnard, K.[Kobus],
Evaluating Image Retrieval,
CVPR05(I: 955-961).
IEEE DOI
HTML Version.
Code, Image Retrieval. Dataset, Image Retrieval.

Zoom It, Seadragon,
2008.
WWW Link. System, Seadragon. Code, Image Pyramids. The Seadragon system was acquired by Microsoft Live Labs. And turned into Zoom.It. The goal is rapid exploration of large image databases. Library for web-based image pyramids.

Photosynth,
2008.
WWW Link. Code, Mosaic. Vendor, Database. Vendor, Panoramic Images. The Photosynth system builds on the Seadragon system but is primarily the University of Washington work on synthesis of scenes Assembles multiple images into a single whole, not exactly mosaicing, but similar. Available for download.
See also Multi-View Stereo for Community Photo Collections.
See also Scene Reconstruction from Community Photo Collections.
See also Microsoft Research.

Unal, M.E.[Mesut Erhan], Ye, K.[Keren], Zhang, M.[Mingda], Thomas, C.[Christopher], Kovashka, A.[Adriana], Li, W.[Wei], Qin, D.F.[Dan-Feng], Berent, J.[Jesse],
Learning to Overcome Noise in Weak Caption Supervision for Object Detection,
PAMI(45), No. 4, April 2023, pp. 4897-4914.
IEEE DOI

Earlier: A2, A3, A5, A6, A7, A8, Only:
Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection,
ICCV19(9685-9694)
IEEE DOI
Code, Captioning.
WWW Link. Training, Object detection, Proposals, Visualization, Detectors, Ovens, Semantics, Language-supervised object detection, vision and language. image classification, learning (artificial intelligence), text analysis, bounding box supervision, Natural languages

Chen, T.L.[Tian-Lang], Deng, J.J.[Jia-Jun], Luo, J.B.[Jie-Bo],
Adaptive Offline Quintuplet Loss for Image-text Matching,
ECCV20(XIII:549-565).
Springer DOI
Code, Retrieval.
WWW Link.

Revaud, J.[Jerome], Almazan, J.[Jon], Rezende, R.[Rafael], de Souza, C.[Cesar],
Learning With Average Precision: Training Image Retrieval With a Listwise Loss,
ICCV19(5106-5115)
IEEE DOI
Code, Image Retrieval.
WWW Link. data mining, image classification, image retrieval, learning (artificial intelligence), object detection, Optimization

Burns, A.[Andrea], Tan, R.[Reuben], Saenko, K.[Kate], Sclaroff, S.[Stan], Plummer, B.A.[Bryan A.],
Language Features Matter: Effective Language Representations for Vision-Language Tasks,
ICCV19(7473-7482)
IEEE DOI
Code, Visualization.
WWW Link. data visualisation, graph theory, image representation, learning (artificial intelligence), Grounding

Xiao, J.B.[Jun-Bin], Shang, X.[Xindi], Yang, X.[Xun], Tang, S.[Sheng], Chua, T.S.[Tat-Seng],
Visual Relation Grounding in Videos,
ECCV20(VI:447-464).
Springer DOI
Code, Relations.
WWW Link.

Yang, Z.Y.[Zheng-Yuan], Chen, T.L.[Tian-Lang], Wang, L.W.[Li-Wei], Luo, J.B.[Jie-Bo],
Improving One-Stage Visual Grounding by Recursive Sub-query Construction,
ECCV20(XIV:387-404).
Springer DOI
Code, Query.
WWW Link.

Bhattacharya, N., Li, Q., Gurari, D.,
Why Does a Visual Question Have Different Answers?,
ICCV19(4270-4279)
IEEE DOI
Code, Visual Q-A.
WWW Link. question answering (information retrieval), visual question answering, Visualization, Powders, Task analysis, Computer vision

Gulshan, V.[Varun],
Implementation of the Self-Similarity Descriptor,
Online2007
WWW Link. Code, Matching.

Uriza, E.[Esteban], Gómez Fernández, F.[Francisco], Rais, M.[Martín],
Efficient Large-scale Image Search With a Vocabulary Tree,
IPOL(8), 2018, pp. 71-98.
DOI Link
Code, Image Retrieval. Code, Image Retrieval, C++. Sivic and Zisserman technique:
See also Efficient Visual Search for Objects in Videos.

Wang, B., Ma, L., Zhang, W., Jiang, W., Wang, J., Liu, W.,
Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network,
ICCV19(2641-2650)
IEEE DOI
Code, Captioning.
WWW Link. image fusion, image representation, image sequences, learning (artificial intelligence), Encoding

Lin, J.[Ji], Gan, C.[Chuang], Wang, K.[Kuan], Han, S.[Song],
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devices,
PAMI(44), No. 5, May 2022, pp. 2760-2774.
IEEE DOI

Earlier: A1, A2, A4, Only:
TSM: Temporal Shift Module for Efficient Video Understanding,
ICCV19(7082-7092)
IEEE DOI
Code, Video Understanding.
WWW Link. Computational modeling, Convolution, Streaming media, Training, Solid modeling, Temporal shift module, video recognition, network dissection. convolutional neural nets, object detection, video signal processing, video streaming, Real-time systems

Xiong, Y., Huang, Q., Guo, L., Zhou, H., Zhou, B., Lin, D.,
A Graph-Based Framework to Bridge Movies and Synopses,
ICCV19(4591-4600)
IEEE DOI
Code, Video Understanding.
WWW Link. entertainment, graph theory, video signal processing, graph-based framework, video analytics, movie understanding, Computer vision

Li, X.J.[Xiu-Jun], Yin, X.[Xi], Li, C.Y.[Chun-Yuan], Zhang, P.C.[Peng-Chuan], Hu, X.W.[Xiao-Wei], Zhang, L.[Lei], Wang, L.J.[Li-Juan], Hu, H.D.[Hou-Dong], Dong, L.[Li], Wei, F.[Furu], Choi, Y.J.[Ye-Jin], Gao, J.F.[Jian-Feng],
OSCAR: Object-Semantics Aligned Pre-Training for Vision-Language Tasks,
ECCV20(XXX: 121-137).
Springer DOI
Code, Learning.
WWW Link.

Qiu, W.C.[Wei-Chao], Yuille, A.L.[Alan L.],
UnrealCV: Connecting Computer Vision to Unreal Engine,
VARVAI16(III: 909-916).
Springer DOI
Code, Virtual Reality.
WWW Link.

Zhang, L., Zhang, D.,
Visual Understanding via Multi-Feature Shared Learning With Global Consistency,
MultMed(18), No. 2, February 2016, pp. 247-259.
IEEE DOI
Kernel Code, Visual Learning. Code is available:
HTML Version.

Wang, H.Z.[Hao-Zhou], Duan, Y.L.[Yu-Lin], Shi, Y.[Yun], Kato, Y.[Yoichiro], Ninomiya, S.[Seishi], Guo, W.[Wei],
EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
Code, Plant Phenotype.

Delconte, F.[Florian], Ngo, P.[Phuc], Kerautret, B.[Bertrand], Debled-Rennesson, I.[Isabelle], Nguyen, V.T.[Van-Tho], Constant, T.[Thiery],
CNN-based Method for Segmenting Tree Bark Surface Singularites,
IPOL(12), 2022, pp. 1-26.
DOI Link
WWW Link. Code, Tree Bark.

Liu, N., Zhao, Q., Zhang, N., Cheng, X., Zhu, J.,
Pose-Guided Complementary Features Learning for Amur Tiger Re-Identification,
CVWC19(286-293)
IEEE DOI
Code, Recognition.
WWW Link. ecology, environmental science computing, feature extraction, image classification, Wildlife Conservation

Yu, J., Su, H., Liu, J., Yang, Z., Zhang, Z., Zhu, Y., Yang, L., Jiao, B.,
A Strong Baseline for Tiger Re-ID and its Bag of Tricks,
CVWC19(302-309)
IEEE DOI
Code, Recognition.
WWW Link. feature extraction, image matching, image sampling, learning (artificial intelligence), object detection, flip as new id

Liu, C., Zhang, R., Guo, L.,
Part-Pose Guided Amur Tiger Re-Identification,
CVWC19(315-322)
IEEE DOI
Code, Recognition.
WWW Link. inference mechanisms, learning (artificial intelligence), pose estimation, PlainID competitions, WildID competitions, Pose Alignment

Shukla, A., Anderson, C., Cheema, G.S.[G. Sigh], Gao, P., Onda, S., Anshumaan, D., Anand, S., Farrell, R.,
A Hybrid Approach to Tiger Re-Identification,
CVWC19(294-301)
IEEE DOI
Code, Recognition.
WWW Link. data analysis, entropy, feature extraction, image classification, image matching, image representation, tigers

Drouyer, S.[Sébastien],
An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases,
IPOL(10), 2020, pp. 105-123.
DOI Link
Survey, Crack Detection. Code, Crack Detection.

Benz, C., Debus, P., Ha, H.K., Rodehorst, V.,
Crack Segmentation on UAS-based Imagery using Transfer Learning,
IVCNZ19(1-6)
IEEE DOI
Code, Crack Detection.
WWW Link. autonomous aerial vehicles, convolutional neural nets, crack detection, image resolution, image segmentation, UAS

Berry, R.[Richard], Burnell, J.[James],
Handbook of Astronomical Image Processing,
Willmann-Bell2005.
HTML Version. Code, Image Processing. Includes the Astronomical Image Processing package.

Paulus, D., and Hornegger, J.,
Pattern Recognition and Image Processing in C++,
ViewegBraunschweig (Germany), 1995 Algorithms. Object-Oriented Programming. Code, Image Processing. Code, Image Processing, C++.
HTML Version.

Fisher, R.B.[Robert B.], (Ed.)
HIPR2: Free WWW-based Image Processing Teaching Materials with JAVA,
Online Book2000.
Edinburgh Code, Image Processing. Code, Image Processing, Java. Department of Artificial Intelligence University of Edinburgh, UK. A collection of Image Processing examples -- with Java to be run through the web. For teaching. The 50 most common classes of image processing operations. How they work, when to use them, examples and java.
WWW Link.

Fisher, R.B.[Robert B.], Perkins, S.[Simon], Walker, A.[Ashley], Wolfart, E.[Erik],
Hypermedia Image Processing Reference,
A complete online tutorial for image processing, available on CD-ROM from John Wiley& Sons, 1996. Code, Image Processing. Demonstration version: Edinburgh
WWW Link.

Parker, J.R.,
Algorithms for Image Processing and Computer Vision,
Wiley1996. ISBN 0-471-14056-2. Code, Image Processing. Chapters include: Advanced edge detection, Use of digital morphology, Advanced methods in grey-level segmentation, Texture, Skeletonization, Image Restoration, Wavelets, OCR, Symbol recognition, GA. Buy this book: Algorithms for Image Processing and Computer Vision

Parker, J.R.,
Practical Computer Vision Using C,
WileyOctober 1993. ISBN: 0-471-59262-5
HTML Version. Code, Computer Vision. Code, Image Processing, C. Buy this book: Practical Computer Vision Using C

Ritter, G.X.[Gerhard X.], and Wilson, J.N.[Joseph N.],
Handbook of Computer Vision Algorithms in Image Algebra,
CRC PressBoca Raton, FL, 1996. ISBN 0-8493-2636-2. Code, Image Processing.
WWW Link.

Umbaugh, S.E.[Scott E.],
Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, Second Edition,
CRC PressBoca Raton, FL, November 15, 2010. ISBN: 9781439802052
WWW Link. Buy this book: Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools, Second Edition Code, Image Processing.

Earlier:
Computer Vision and Image Processing: A Practical Approach Using CVIPtools,
New York: Prentice Hall1998. ISBN 0-13-264599-8, with a CD-ROM. Code, Image Processing. Application oriented. Analysis, Restoration, Enhancement, Compression.

Jähne, B.[Bernd], Haussecker, H.W.[Horst W.], and Geissler, P., (eds.),
Handbook of Computer Vision and Applications. 1. Sensors and Imaging,
Academic PressSan Diego, CA, 1999.
And:
Handbook of Computer Vision and Applications. 2. Signal Processing and Pattern Recognition,
Academic PressSan Diego, CA, 1999.
And:
Handbook of Computer Vision and Applications. 3. Systems and Applications,
Academic PressSan Diego, CA, 1999. Three volumes and CD-ROM set, xxiii + 623 + xxiii + 942 + xlv + 894 pp., $995. ISBN 0-12-379770-5. Indexed under: HCVA99 Code, Computer Vision. Buy this book: Handbook Of Computer Vision And Applications 3 Vol Set And Cd-rom Set

Jähne, B.[Bernd], Haussecker, H.W.[Horst W.],
Computer Vision and Applications: A Guide for Students and Practitioners,
Academic PressApril 2000, 693 pp, ISBN: 978-0-12-379777-3. Code, Computer Vision.
WWW Link. A concise edition based on the 3 volume set above. The main parts parallel the volumes above. Buy this book: Computer Vision and Applications: A Guide for Students and Practitioners (With CD-ROM)

Nikolaidis, N.[Nikos], Pitas, I.[Ioannis],
3-D Image Processing Algorithms,
John Wiley& Sons, October 2000. ISBN: 0471377368. Analysis of 3-D data (volume filtering, features of 3-D objects, etc.) With the EIKONA3D software:
WWW Link. Code, Image Processing. Buy this book: 3-D Image Processing Algorithms

Whelan, P.F., and Molloy, D.,
Machine Vision Algorithms in Java: Techniques and Implementation,
SpringerLondon, October 2000. ISBN: 1-85233-218-2.
WWW Link. or
WWW Link. Code, Image Processing. Code, Image Processing, Java. Buy this book: Machine Vision Algorithms in Java: Techniques and Implementation

Ma, Y., Soatto, S., Kosecká, J., Sastry, S.S.,
An Invitation to 3-D Vision: From Images to Geometric Models,
Springer-Verlag2005 ISBN: 0-387-00893-4.
Springer DOI Code, Computer Vision. Buy this book: An Invitation to 3-D Vision

Paragios, N.[Nikos], Chen, Y.M.[Yun-Mei], Faugeras, O.D.[Olivier D.],
Handbook of Mathematical Methods in Computer Vision,
Springer2006, ISBN 978-0-387-26371-7 Indexed as: MMCV05 Code, Image Processing.
WWW Link. Discourse on the mathematical models used in computational vision. Topical areas include: image reconstruction, segmentation and object extraction, shape modeling and registration, motion analysis and tracking, and 3D from images, geometry and reconstruction. The book also includes a study of applications in medical image analysis. Buy this book: Handbook of Mathematical Models in Computer Vision

Paragios, N.[Nikos], Chen, Y.M.[Yun-Mei], and Faugeras, O.D.[Olivier D.], (Eds.)
Handbook of Mathematical Models in Computer Vision,
Springer2005. ISBN 0387263713
WWW Link. Code, Computer Vision. Variational Techniques, Boundary Extraction, Segmentation and Grouping, Shape Modeling and Registration. Buy this book: Handbook of Mathematical Models in Computer Vision

Rovenski, V.[Vladimir],
Modeling of Curves and Surfaces with MATLAB®,
Springer2010, ISBN: 978-0-387-71277-2
Springer DOI Code, Surfaces, Matlab. Buy this book: Modeling of Curves and Surfaces with MATLAB® (Springer Undergraduate Texts in Mathematics and Technology)

Corke, P.[Peter],
Robotics, Vision and Control: Fundamental Algorithms in MATLAB,
SpringerNew-York, 2011. ISBN: 978-3-642-20143-1. Online only.
Springer DOI Buy this book: Robotics, Vision and Control: Fundamental Algorithms in MATLAB (Springer Tracts in Advanced Robotics) Code, Computer Vision. Code, Computer Vision, Matlab.

Marques, O.[Oge],
Practical Image and Video Processing Using MATLAB,
WileySeptember 2011.
ISBN: 978-0-470-82849-6
HTML Version.
Code, Video Processing. Code, Video Processing, Matlab.

Radke, R.J.[Richard J.],
Computer Vision for Visual Effects,
Cambridge University Press2012. ISBN-10: 0521766877 ISBN-13: 978-0521766876

WWW Link.
Code, Visual Effects. Visual effects, some code.

Kanatani, K.[Kenichi], Sugaya, Y.[Yasuyuki], Kanazawa, Y.,
Guide to 3D Vision Computation: Geometric Analysis and Implementation,
SpringerInternational, Cham, Switzerland, December 29, 2016. ISBN 978-3-319-48492-1 (hardcover), ISBN 978-3-319-48943-8.
Springer DOI
Code, 3D Vision. Chapters: 1. Introduction 2. Ellipse Fitting 3. Fundamental Matrix Computation 4. Triangulation 5. 3D Reconstruction from Two Views 6. Homography Computation 7. Planar Triangulation 8. 3D Reconstruction of a Plane 9. Ellipse Analysis and 3D Computation of Circles 10. Multiview Triangulation 11. Bundle Adjustment 12. Self-calibration of Affine Cameras 13. Self-calibration of Perspective Cameras 14. Accuracy of Geometric Estimation 15. Maximum Likelihood of Geometric Estimation 16. Theoretical Accuracy Limit.

Pavlidis, T.,
Algorithms for Graphics and Image Processing,
Rockville MD: Computer Science Press1982. Survey, Algorithms. Algorithms, Survey. Code, Image Processing. The book has basic algorithms for many standard image processing tasks. Topics: Digitization, processing, segmentation, projection, data structures, binary images, contour filling, thinning, curve fitting, surface fitting, 2-D graphics, polygon clipping, 3-D graphics.

Stearns, S.D.[Samuel D.], David, R.A.[Ruth A.],
Signal Processing Algorithms in MATLAB,
Prentice Hall1996. ISBN 0-13-045154-1. Code, Signal Processing. Code, Image Processing, Matlab.
And:
Signal Processing Algorithms in Fortran and C,
Prentice Hall1993. Code, Signal Processing.
And: Code, Image Processing, C.
Signal Processing Algorithms,
Prentice Hall1988.

Lindley, C.A.,
Practical Image Processing in C,
WileyNew York, 1991. Source code included on floppy. Code, Image Processing. Code, Image Processing, C.

Svoboda, T.[Tomas], Kybic, J.[Jan], Hlavac, V.[Vaclav],
Image Processing, Analysis and and Machine Vision: A MATLAB Companion,
Cengage2008. ISBN-10: 0495295957. ISBN-13: 9780495295952 Code, Image Processing. Code, Image Processing, Matlab. Buy this book: Image Processing, Analysis & and Machine Vision - A MATLAB Companion

Pitas, I.[Ioannis],
Digital Image Processing Algorithms and Applications,
John Wiley& Sons, Inc. February 2000. ISBN: 0-471-37739-2. Multimedia material for the book:
WWW Link. Code, Image Processing. Buy this book: Digital Image Processing Algorithms and Applications

Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.,
Numerical Recipes in C: The Art of Scientific Computing,
Cambridge University Press1993, ISBN 0521431085. Code, Numerical Algorithms. Code, Image Processing, C.
WWW Link. Buy this book: Numerical Recipes in C: The Art of Scientific Computing

Pratt, W.K.[William K.],
PIKS Foundation C Programmer's Guide,
Manning Publications1995. ISBN 0-13-172339-1. Guide for using the Programmer's Imaging Kernel System standard. Code, Image Processing. Code, Image Processing, C. Buy this book: Piks Foundation: A C Programmer's Guide

Kabir, I.[Ihtisham],
High Performance Computer Imaging,
Manning/ Prentice HallAugust 1996. ISBN 0-13-268301-6 (Initial reference gave: 1-884777-26-0). 378 pp. Code, Image Processing. Code, Image Processing, C. Includes a large selection of C code. Intended for the practical user who wants to implement computer imaging techniques. Details and a sample:
HTML Version. Buy this book: High Performance Computer Imaging

Klette, R.[Reinhard], Zamperoni, P.[Piero],
Handbook of Image Processing Operators,
John Wiley& Sons, 1996. ISBN 0-471-95642-2. Code, Image Processing. Code dealing with image processing defined as mappings of images onto images. This is a translation from the original German book. Buy this book: Handbook of Image Processing Operators

Russ, J.C.[John C.],
The Image Processing Handbook,
CRC PressBoca Raton, FL, Sixth Edition, April 7, 2011. ISBN: 9781439840450.
WWW Link. Buy this book: The Image Processing Handbook, Sixth Edition
Earlier: CRC PressBoca Raton, FL, Fifth Edition, 2007. ISBN: 9780849372544. Code, Image Processing.
WWW Link.
Earlier: CRC PressBoca Raton, FL, Third Edition, 1999. ISBN 0-8493-2532-3.
And: IEEE_PressNew York, 1994, Second Edition.
And: CRC Press1995. ISBN 0-8493-2516-1.

Lyon, D.A.[Doublas A.],
Image Processing in Java,
Prentice Hall1999. ISBN 0-13-974577-7. Fundamentals and java code. Code, Image Processing. Code, Image Processing, Java. Buy this book: Image Processing in Java

Efford, N.[Nick],
Digital Image Processing: A Practical Introduction Using Java,
Addison Wesley2000. ISBN 0201596237. Code, Image Processing. Code, Image Processing, Java. Buy this book: Digital Image Processing: A Practical Introduction Using Java (With CD-ROM)

Rodrigues, L.H.[Lawrence H.],
Building Imaging Applications with Java(TM),
Addison Wesley2001. ISBN 0201700743. Code, Image Processing. Code, Image Processing, Java. Buy this book: Building Imaging Applications with Java(TM) Technology: Using AWT Imaging, Java 2D(TM), and Java(TM) Advanced Imaging (JAI)

Seul, M.[Michael], O'Gorman, L.[Lawrence], Sammon, M.J.[Michael J.],
Practical Algorithms for Image Analysis: Description, Examples, and Code,
Cambridge University2001. ISBN 0521660653.
WWW Link. Code, Image Processing. Buy this book: Practical Algorithms for Image Analysis: Descriptions, Examples, and Code

Gonzalez, R.C.[Rafael C.], Woods, R.E.[Richard E.], Eddins, S.L.[Steven L.],
Digital Image Processing Using MATLAB(R), 2nd Edition,
Gatesmark Publishing2009. ISBN: 9780982085400.
HTML Version. Buy this book: Digital Image Processing Using MATLAB, 2nd ed. Code, Image Processing. Code, Image Processing, Matlab.
Earlier:
Digital Image Processing Using MATLAB(R),
Prentice Hall2004 ISBN: 0-13-008519-7
HTML Version.
HTML Version. Buy this book: Digital Image Processing Using MATLAB

Blanchet, G.[Gerard], Charbit, M.[Maurice],
Digital Signal and Image Processing Using MATLAB(R),
ISTE Ltd2006. Code, Image Processing. Code, Image Processing, Matlab. ISBN: 1905209134. Buy this book: Digital Signal and Image Processing Using MATLAB (Digital Signal and Image Processing series)

Burger, W.[Wilhelm], and Burge, M.J.[Mark J.],
Digital Image Processing: An Algorithmic Approach Using Java,
Springer2007. ISBN 1846283795 and ISBN 3540309403. Code, Image Processing. Code, Image Processing, Java. Buy this book: Digital Image Processing: An Algorithmic Introduction using Java Or the German version: Buy this book: Digitale Bildverarbeitung: Eine Einführung mit Java und ImageJ (X.media.press)

Burger, W.[Wilhelm], Burge, M.J.[Mark J.],
Principles of Digital Image Processing: Core Algorithms,
Springer2009, ISBN: 978-1-84800-194-7
Springer DOI Supplemtary information:
WWW Link. Code, Image Processing. Code, Image Processing, Java. Second in the series. Color, comparing images, curve and corner, Fourier and Cosine transforms, Regions techniques. Buy this book: Principles of Digital Image Processing: Core Algorithms (Undergraduate Topics in Computer Science)

Burger, W.[Wilhelm], Burge, M.J.[Mark J.],
Principles of Digital Image Processing: Fundamental Techniques,
Springer2009, ISBN: 978-1-84800-190-9
Springer DOI
Third in the series. Edges, contours, Filters (linear, nonlinear), Image morphology, Image statistics, Image types and formats. Supplemtary information:
WWW Link. Code, Image Processing. Code, Image Processing, Java. Buy this book: Principles of Digital Image Processing: Fundamental Techniques (Undergraduate Topics in Computer Science)

Wang, M.Q.[Mei-Qing], Lai, C.H.[Choi-Hong],
A Concise Introduction to Image Processing using C++,
CRC PressNovember 2008, ISBN: 9781584888970 Code, Image Processing. Code, Image Processing, C++.
WWW Link. Buy this book: A Concise Introduction to Image Processing using C++ (Chapman & Hall/Crc Numerical Analysis and Scientific Computing)

Qidwai, U.[Uvais], Chen, C.H.,
Digital Image Processing: An Algorithmic Approach with MATLAB,
CRC PressOctober 2009, ISBN: 9781420079500. Code, Image Processing. Code, Image Processing, Matlab.
WWW Link. Buy this book: Digital Image Processing: An Algorithmic Approach with MATLAB (Chapman & Hall/Crc Textbooks in Computing)

Chaira, T.[Tamalika], Ray, A.K.[Ajoy Kumar],
Fuzzy Image Processing and Applications with MATLAB,
CRC PressNovember 2009, ISBN: 9781439807088. Code, Image Processing. Code, Image Processing, Matlab.
WWW Link. Buy this book: Fuzzy Image Processing and Applications with MATLAB

Demirkaya, O.[Omer], Asyali, M.H.[Musa H.], Sahoo, P.K.[Prasanna K.],
Image Processing with MATLAB: Applications in Medicine and Biology,
CRC PressSeptember 2008, ISBN: 9780849392467. Code, Image Processing. Code, Image Processing, Matlab.
WWW Link. Buy this book: Image Processing with MATLAB: Applications in Medicine and Biology (MATLAB Examples)

Solomon, C.[Chris], Breckon, T.[Toby], (Eds.)
Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab,
WileyFebruary 2011.
ISBN: 978-0-470-84472-4
HTML Version. Also:
WWW Link.
Code, Image Processing. Code, Image Processing, Matlab.

Frery, A.C.[Alejandro C.], Perciano, T.[Talita],
Introduction to Image Processing Using R: Learning by Examples,
Springer2013. ISBN 978-1-4471-4949-1

Springer DOI
Code, Image Processing.

Min, J.[Jaesik], Powell, M.W.[Mark W.], Bowyer, K.W.[Kevin W.],
Automated Performance Evaluation of Range Image Segmentation Algorithms,
SMC-B(34), No. 1, February 2004, pp. 263-271.
IEEE Abstract.

Earlier:
Automated Performance Evaluation of Range Image Segmentation,
WACV00(163-168).
IEEE DOI
Code, Segmenation Evaluation.
HTML Version.

OSGeo: Open Source Geospatial Foundation,
2009. Code, Geospatial. Code, GIS. Code, Open Source.
WWW Link. Data access, mapping, public geospatial data, GIS.
See also LibTIFF: TIFF Library and Utilities.

Kaufmann, E.[Elia], Loquercio, A.[Antonio], Ranftl, R.[René], Müller, M.[Matthias], Koltun, V.[Vladlen], Scaramuzza, D.[Davide],
Deep Drone Acrobatics,
RSS20(xx-yy).
PDF File.
Code, Drones.
WWW Link. Video:
WWW Link. RSS20 presentation:
WWW Link.

Zhang, P., Zhong, Y., Li, X.,
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications,
VisDrone19(37-45)
IEEE DOI
Code, UAV.
WWW Link. autonomous aerial vehicles, embedded systems, feature extraction, feedforward neural nets, learning (artificial intelligence), UAV

Canty, M.J.[Morton J.],
Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL,
Second Edition: CRC PressDecember 2009, ISBN: 9781420087130 Buy this book: Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition First edition: CRC PressAugust, 2006, ISBN: 9780849372513
WWW Link. Code, Image Processing.

Baron, A.F.[Anne-Flore], Boulant, O.[Olivier], Panico, I.[Ivan], Vayatis, N.[Nicolas],
A Compartmental Epidemiological Model Applied to the Covid-19 Epidemic,
IPOL(11), 2021, pp. 105-119.
DOI Link
Code, Epidemic Model.

Behrendt, K., Soussan, R.,
Unsupervised Labeled Lane Markers Using Maps,
CVRSUAD19(832-839)
IEEE DOI
Code, Lane Detection.
WWW Link. image segmentation, optimisation, regression analysis, unsupervised learning, high-quality lane marker datasets, dataset

Chen, J., Liu, C., Wu, J., Furukawa, Y.,
Floor-SP: Inverse CAD for Floorplans by Sequential Room-Wise Shortest Path,
ICCV19(2661-2670)
IEEE DOI
Code, Indoor Model.
WWW Link. CAD, computational complexity, graph theory, image colour analysis, image reconstruction, neural nets, optimisation, inverse CAD, Image segmentation

Zioulis, N.[Nikolaos], Alvarez, F.[Federico], Zarpalas, D.[Dimitrios], Daras, P.[Petros],
Single-shot cuboids: Geodesics-based end-to-end Manhattan aligned layout estimation from spherical panoramas,
IVC(110), 2021, pp. 104160.
Elsevier DOI
Code, Layout Extimation.
WWW Link. Panoramic scene understanding, Indoor 3D reconstruction, Layout estimation, Spherical panoramas, Omnidirectional vision

Bernabé, S.[Sergio], García, C.[Carlos], Igual, F.D.[Francisco D.], Botella, G.[Guillermo], Prieto-Matias, M.[Manuel], Plaza, A.[Antonio],
Portability Study of an OpenCL Algorithm for Automatic Target Detection in Hyperspectral Images,
GeoRS(57), No. 11, November 2019, pp. 9499-9511.
IEEE DOI
Code, Target Detection. Hyperspectral imaging, Object detection, Performance evaluation, Graphics processing units, portability

Garris, M.D.,
Nist Form-Based Handprint Recognition System (Release 2.2),
NISTIRApril 2003.
HTML Version. Code, OCR. Standard reference form-based handprint recognition system for evaluating optical character recognition.

Section, Multiple Entries: 25.4.10 Bar Code Readers, Reading Chapter Contents (Back)
Bar Codes. Barcodes. QR Codes. Barcodes.

Lazzara, G.[Guillaume], Levillain, R.[Roland], Geraud, T.[Thierry], Jacquelet, Y.[Yann], Marquegnies, J.[Julien], Crepin-Leblond, A.[Arthur],
The SCRIBO Module of the Olena Platform: A Free Software Framework for Document Image Analysis,
ICDAR11(252-258).
IEEE DOI
Code, Document Analysis.

Gamera project,
Online2007.
WWW Link. Code, Document Analysis. A framework for the creation of structured document analysis applications by domain experts.

Nguyen, D.T.N.,
State-of-the-Art in Action: Unconstrained Text Detection,
RLQ19(1104-1111)
IEEE DOI
Code, Text Detection.
WWW Link. feature extraction, text detection, textual information detection, test data annotation, post processing

Xing, L.J.[Lin-Jie], Tian, Z.[Zhi], Huang, W.L.[Wei-Lin], Scott, M.[Matthew],
Convolutional Character Networks,
ICCV19(9125-9135)
IEEE DOI
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, image recognition, iterative methods, recurrent neural nets, text detection, Neural networks

Dalitz, C.[Christoph], Karsten, T.[Thomas], Droettboom, M.[Michael], Fujinaga, I.[Ichiro], Pose, F.[Florian], Czerwinski, B.[Bastian],
Staff Line Removal Toolkit for Gamera,
Online2005-2007.
WWW Link. Code, Music Processing.

Pan, J.S.[Jeng-Shyang], Huang, H.C.[Hsiang-Cheh], Jain, L.C.[Lakhmi C.],
Intelligent Watermarking Techniques,
World ScientificFebruary 2004 ISBN: 978-981-238-757-8 (With CD-Rom) Survey, Watermark. Code, Watermark.
HTML Version. Buy this book: Intelligent Watermarking Techniques (Innovative Intelligence)

Ehret, T.[Thibaud],
Automatic Detection of Internal Copy-Move Forgeries in Images,
IPOL(8), 2018, pp. 167-191.
DOI Link
Code, Forgery Detection.

Gardella, M.[Marina], Musé, P.[Pablo],
Forensic Similarity for Source Camera Model Comparison,
IPOL(12), 2022, pp. 480-489.
DOI Link
Code, Forensic Similarity.

Gardella, M.[Marina], Musé, P.[Pablo],
Image Forgery Detection via Forensic Similarity Graphs,
IPOL(12), 2022, pp. 490-500.
DOI Link
Code, Forgery.

GOCR,
2002. Open Source OCR.
WWW Link. Code, OCR.

Google Tesseract-OCR,
1995 OCR originally developed at HP.
WWW Link. Code, OCR.

Ferradans, S.[Sira], Palma-Amestoy, R., Provenzi, E.,
An Algorithmic Analysis of Variational Models for Perceptual Local Contrast Enhancement,
IPOL(5), 2015, pp. 219-233.
DOI Link
Code, Color Enhancement.
See also Perceptually Inspired Variational Framework for Color Enhancement, A.

Gomila Salas, J.G.[Juan Gabriel], Lisani, J.L.[Jose Luis],
Local Color Correction,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Color Correction.

Wang, G.Q.[Guo-Qing], Sun, C.M.[Chang-Ming], Sowmya, A.[Arcot],
Context-Enhanced Representation Learning for Single Image Deraining,
IJCV(129), No. 5, May 2021, pp. 1650-1674.
Springer DOI

Earlier:
ERL-Net: Entangled Representation Learning for Single Image De-Raining,
ICCV19(5643-5651)
IEEE DOI
Code, Image Restoration.
WWW Link. image enhancement, image restoration, learning (artificial intelligence), single image deraining, Image restoration

Limare, N.[Nicolas], Lisani, J.L.[Jose-Luis], Morel, J.M.[Jean-Michel], Petro, A.B.[Ana Belén], Sbert, C.[Catalina],
Simplest Color Balance,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Color Balance.

Funt, B.V., Ciurea, F., McCann, J.J.,
Retinex in Matlab,
JEI(13), No. 1, January 2004, pp. 48-57.
HTML Version. Code, Retinex. Code, Retinex, Matlab.
WWW Link.

Petro, A.B.[Ana Belén], Sbert, C.[Catalina], Morel, J.M.[Jean-Michel],
Multiscale Retinex,
IPOL(2014), No. 1, 2014, pp. 71-88.
DOI Link
Code, Retinex.

Limare, N.[Nicolas], Petro, A.B.[Ana Belén], Sbert, C.[Catalina], Morel, J.M.[Jean-Michel],
Retinex Poisson Equation: a Model for Color Perception,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Retinex.

Lisani, J.L.[Jose-Luis], Petro, A.B.[Ana-Belén], Sbert, C.[Catalina],
Center/Surround Retinex: Analysis and Implementation,
IPOL(11), 2021, pp. 434-450.
DOI Link
Code, Retinex. An implementation of Land model.
See also alternative technique for the computation of the designator in the retinex theory of color vision, An.

Radar Tools,
2006.
WWW Link. Code, Radar. The Berlin group.

Ozdemir, C.[Caner],
Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms,
WileyMarch 2012.
ISBN: 978-0-470-28484-1
HTML Version.
Code, SAR. Code, SAR, Matlab.

Gomez, L.[Luis], Wu, J.[Jie], Frery, A.C.[Alejandro C.],
Non-Local Means Filters for Full Polarimetric Synthetic Aperture Radar Images with Stochastic Distances,
IPOL(12), 2022, pp. 142-172.
DOI Link
Code, SAR Filters.

Morishita, Y.[Yu], Lazecky, M.[Milan], Wright, T.J.[Tim J.], Weiss, J.R.[Jonathan R.], Elliott, J.R.[John R.], Hooper, A.[Andy],
LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link
Code, InSAR.

Zhang, C.B.[Chang-Bin], Jiang, P.T.[Peng-Tao], Hou, Q.B.[Qi-Bin], Wei, Y.C.[Yun-Chao], Han, Q.[Qi], Li, Z.[Zhen], Cheng, M.M.[Ming-Ming],
Delving Deep Into Label Smoothing,
IP(30), 2021, pp. 5984-5996.
IEEE DOI
WWW Link.
Code, Regularization. Training, Predictive models, Noise measurement, Smoothing methods, Tools, Robustness, Cats, Regularization, classification, soft labels, noisy labels

Mathematical Morphology,
OnlineAugust, 1998.
WWW Link. Code, Morphology. Code, Visualization. Khoros code for morphology.

Blusseau, S.[Samy], Velasco-Forero, S.[Santiago], Angulo, J.[Jesús], Bloch, I.[Isabelle],
Adaptive Anisotropic Morphological Filtering Based on Co-Circularity of Local Orientations,
IPOL(12), 2022, pp. 111-141.
DOI Link
Code, Morphology.
Earlier:
Tropical and Morphological Operators for Signals on Graphs,
ICIP18(1198-1202)
IEEE DOI
Morphology, Tensile stress, Algebra, Image processing, Lattices, Anisotropic magnetoresistance, Nickel, Mathematical morphology, tropical algebra

Qiao, Z.N.[Zhi-Nan], Yuan, X.H.[Xiao-Hui], Zhuang, C.Y.[Cheng-Yuan], Meyarian, A.[Abolfazl],
Attention Pyramid Module for Scene Recognition,
ICPR21(7521-7528)
IEEE DOI
Code, Recognition.
WWW Link. Vocabulary, Image recognition, Semantics, Pipelines, Benchmark testing, Streaming media, feature pyramid

Mondelli, M.[Marco], Ciomaga, A.[Adina],
Finite Difference Schemes for MCM and AMSS,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Scale Space.

Sarkar, S., and Boyer, K.L.,
Computing Perceptual Organization in Computer Vision,
World Scientific1994. (ISBN: 981-02-1832-X). 232pp. Book Code, Perceptual Grouping. Code:
HTML Version. Based on Sarkar's thesis. Derive a framework for perceptual organization at various levels. lower levels feed higher levels. Does not get to the recognition level.

Jacobs, D.W.,
Robust and Efficient Detection of Salient Convex Groups,
PAMI(18), No. 1, January 1996, pp. 23-37.
IEEE DOI Code, Convex Grouping. Code:
WWW Link.
Earlier:
Robust and efficient detection of convex groups,
CVPR93(770-771).
IEEE DOI Groupings of line segments into convex objects. For finding m groups in n lines, the algorithm is (n^2)log(n)+nm

Soundararajan, P.[Padmanabhan], Sarkar, S.[Sudeep],
An in-depth study of graph partitioning measures for perceptual organization,
PAMI(25), No. 6, June 2003, pp. 642-660.
IEEE Abstract.
Evaluation, Segmentation.
WWW Link. Code, Perceptual Grouping. Dataset, Perceptual Grouping.
Earlier:
Empirical evaluation of graph partitioning measures for perceptual organization,
EEMCV01(xx-yy).
Quality of groups generated by minimum (
See also Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation, An. ) or average (
See also Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata. ) or normalized (
See also Normalized Cuts and Image Segmentation. ) cuts are equivalent for recognition.

Wang, S.[Song],
Ratio Contour Code,
Online2006. Code, Segmentation.
WWW Link. Also ratio cut, segmentation benchmark code, symmetric boundary extraction.

Blusseau, S.[Samy], von Gioi, R.G.[Rafael Grompone],
Generation and Detection of Alignments in Gabor Patterns,
IPOL(6), 2016, pp. 268-299.
DOI Link
Code, Saliency.

Tendero, Y.[Yohann],
The Flutter Shutter Camera Simulator,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Flutter Shutter.

Tendero, Y.[Yohann],
The Flutter Shutter Code Calculator,
IPOL(5), 2015, pp. 234-256.
DOI Link
Code, Flutter Shutter.

Wang, T., Piao, Y., Lu, H., Li, X., Zhang, L.,
Deep Learning for Light Field Saliency Detection,
ICCV19(8837-8847)
IEEE DOI
Code, Saliency.
WWW Link. feature extraction, image representation, image sampling, image segmentation, learning (artificial intelligence), Image color analysis

Gutiérrez, J.[Jesús], David, E.[Erwan], Rai, Y.[Yashas], Le Callet, P.[Patrick],
Toolbox and dataset for the development of saliency and scanpath models for omnidirectional/360° still images,
SP:IC(69), 2018, pp. 35-42.
Elsevier DOI
Code, Omnidirectional Images. Omnidirectional images, Dataset, Toolbox, Eye-tracking, Saliency, Scanpath, 360° images

Zhou, T.[Tan], Popescu, S.[Sorin],
waveformlidar: An R Package for Waveform LiDAR Processing and Analysis,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
Code, LIDAR Processing.

Gruber, T., Julca-Aguilar, F., Bijelic, M., Heide, F.,
Gated2Depth: Real-Time Dense Lidar From Gated Images,
ICCV19(1506-1516)
IEEE DOI
Code, LIDAR.
WWW Link. cameras, image resolution, image sampling, image sensors, optical radar, dense depth camera, learning depth, gated images, Real-time systems

Akiki, R.[Roland], de Franchis, C.[Carlo], Facciolo, G.[Gabriele], Morel, J.M.[Jean-Michel], Grandin, R.[Raphaël],
Phase Unwrapping using a Joint CNN and SQD-LSTM Network,
IPOL(12), 2022, pp. 378-388.
DOI Link
Code, Phase Unwrapping.

Rajaei, B.[Boshra], Gigan, S.[Sylvain], Krzakala, F.[Florent], Daudet, L.[Laurent],
Robust Phase Retrieval with the Swept Approximate Message Passing (prSAMP) Algorithm,
IPOL(7), 2017, pp. 43-55.
DOI Link
Code, Phase Retrieval.

Mega Wave,
2004,
WWW Link. Wavelet information and code. Code, Wavelets. Code, Snakes.

Donoho, D.L.[David L.], Duncan, M.R.[Mark Reynold], Huo, X.M.[Xiao-Ming], Levi, O.[Ofer],
Wavelab,
Online Book1999.
WWW Link. Code, Wavelets. Code, Wavelets, Matlab. A collection of Matlab functions to implement various algorithms for wavelet analysis.

Yu, G.S.[Guo-Shen], Sapiro, G.[Guillermo],
DCT image denoising: a simple and effective image denoising algorithm,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Denoising.
See also Ideal spatial adaptation via wavelets shrinkage.

Rajaei, B.[Boshra],
An Analysis and Improvement of the BLS-GSM Denoising Method,
IPOL(2014), No. 2014, pp. 44-70.
DOI Link
Code, Denoising. Bayesian least squares, Gaussian scale mixture
See also Image denoising using scale mixtures of gaussians in the wavelet domain.

Salmona, A.[Antoine], Bouza, L.[Lucía], Delon, J.[Julie],
DeOldify: A Review and Implementation of an Automatic Colorization Method,
IPOL(12), 2022, pp. 347-368.
DOI Link
Code, Colorization. CNN based colorization.

van de Sande, K.E.A.[Koen E. A.], Gevers, T.[Theo], Snoek, C.G.M.[Cees G. M.],
Evaluating Color Descriptors for Object and Scene Recognition,
PAMI(32), No. 9, September 2010, pp. 1582-1596.
IEEE DOI
Code, Color Descriptors.
Earlier:
Evaluation of color descriptors for object and scene recognition,
CVPR08(1-8).
IEEE DOI

And:
A comparison of color features for visual concept classification,
CIVR08(141-150).
For retrieval, colors change under different lighting. What film photographers have always known. Software used is available:
WWW Link. Overall OpponentSIFT was best if there is no other information. (SIFT defined on the Opponent color transforms).

AQSENSE,
1989.
WWW Link. Vendor, 3-D Shape. Code, 3-D Shape. Code for 3-D shape descriptions and inspection. Match 3-D, XYZ from range maps, merge range maps. The SAL3D (3D Shape Analysis Library) product.

libE57: software tools for managing E57 files,
OnlineDecember 11, 2010.
WWW Link. Code, 3D Data.

Digne, J.[Julie],
An Analysis and Implementation of a Parallel Ball Pivoting Algorithm,
IPOL(2014), No. 1, pp. 149-168.
DOI Link
Code, 3D Reconstruction. Surface reconstruction from a set of 3D points with coordinates and oriented normals.
See also Ball-Pivoting Algoritm for Surface Reconstruction, The.

Littwin, G., Wolf, L.B.,
Deep Meta Functionals for Shape Representation,
ICCV19(1824-1833)
IEEE DOI
Code, 3D.
WWW Link. image classification, image reconstruction, image representation, image resolution, neural nets, shape recognition,

Treece, G.M.[Graham M.],
VolMorph Documentation,
Online2005.
HTML Version. Code, Mesh Models. Generating and visualising morphing sequences from one polygonal mesh to another.

Allen, B.[Brett],
ply2vri,
Online2002.
WWW Link. Code, Mesh Models. Manipulate mesh models

Digne, J.[Julie],
An Implementation and Parallelization of the Scale Space Meshing Algorithm,
IPOL(5), 2015, pp. 282-295.
DOI Link
Code, Mesh Generation. Ball Pivoting Algorithm which is linked to patent US6968299B1.
See also Ball-Pivoting Algoritm for Surface Reconstruction, The.
See also Analysis and Implementation of a Parallel Ball Pivoting Algorithm, An.

Turk, G., Levoy, M.,
Zippered Polygon Meshes from Range Images,
SIGGraph-94(311-318). Code, Mesh Models.
WWW Link.

Liu, S.C.[Shi-Chen], Li, T.Y.[Tian-Ye], Chen, W.K.[Wei-Kai], Li, H.[Hao],
A General Differentiable Mesh Renderer for Image-Based 3D Reasoning,
PAMI(44), No. 1, January 2022, pp. 50-62.
IEEE DOI

Earlier: A1, A3, A2, A4:
Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning,
ICCV19(7707-7716)
IEEE DOI
Code, Rendering.
WWW Link. Rendering (computer graphics), Standards, Cognition, Task analysis, picture/image generation. image colour analysis, image reconstruction, image representation, inference mechanisms,

Huang, Z.W.[Zhe-Wei], Zhou, S.C.[Shu-Chang], Heng, W.[Wen],
Learning to Paint With Model-Based Deep Reinforcement Learning,
ICCV19(8708-8717)
IEEE DOI
Code, Artistic.
WWW Link. art, human computer interaction, image colour analysis, image texture, learning (artificial intelligence), Rendering (computer graphics)

Delbracio, M.[Mauricio], Musé, P.[Pablo], Buades, A.[Antoni], Morel, J.M.[Jean-Michel],
Accelerating Monte Carlo Renderers by Ray Histogram Fusion,
IPOL(5), 2015, pp. 55-72.
DOI Link
Code, Rendering.

Ciomaga, A.[Adina], Monasse, P.[Pascal], Morel, J.M.[Jean-Michel],
The Image Curvature Microscope: Accurate Curvature Computation at Subpixel Resolution,
IPOL(7), 2017, pp. 197-217.
DOI Link
Code, Curvature.
Earlier:
Level lines shortening yields an image curvature microscope,
ICIP10(4129-4132).
IEEE DOI

Dalitz, C.[Christoph], Wilberg, J.[Jens], Aymans, L.[Lukas],
TriplClust: An Algorithm for Curve Detection in 3D Point Clouds,
IPOL(9), 2019, pp. 26-46.
DOI Link
Code, Curve Detection. Detecting and separating curves in 3D point clouds without making a priori assumptions about their parametric shape.
See also Iterative Hough Transform for Line Detection in 3D Point Clouds.

Xu, M.[Mutian], Ding, R.[Runyu], Zhao, H.S.[Heng-Shuang], Qi, X.J.[Xiao-Juan],
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds,
CVPR21(3172-3181)
IEEE DOI
WWW Link. Code, Point Cloud Convolutions. Convolution, Computational modeling, Pipelines, Network architecture, Pattern recognition

Wu, C.H.[Cheng-Hao], Hsu, C.F.[Chih-Fan], Hung, T.K.[Tzu-Kuan], Griwodz, C.[Carsten], Ooi, W.T.[Wei Tsang], Hsu, C.H.[Cheng-Hsin],
Quantitative Comparison of Point Cloud Compression Algorithms With PCC Arena,
MultMed(25), 2023, pp. 3073-3088.
IEEE DOI
Code, Point Cloud. we propose an open-source benchmark platform called PCC Arena

Qiu, S.[Shi], Wu, Y.F.[Yun-Fan], Anwar, S.[Saeed], Li, C.Y.[Chong-Yi],
Investigating Attention Mechanism in 3D Point Cloud Object Detection,
3DV21(403-412)
IEEE DOI
WWW Link.
Code, Object Detection. Point cloud compression, Service robots, Pipelines, Object detection, Transformers, Reliability engineering

Zheng, T., Chen, C., Yuan, J., Li, B., Ren, K.,
PointCloud Saliency Maps,
ICCV19(1598-1606)
IEEE DOI
Code, Saliency.
WWW Link. convolutional neural nets, image classification, image representation, image segmentation, object detection, DGCNN, Predictive models

Li, J.X.[Jia-Xin], Lee, G.H.[Gim Hee],
USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds,
ICCV19(361-370)
IEEE DOI
Code, Interest Pointe.
WWW Link. feature extraction, learning (artificial intelligence), object detection, probability, Solid modeling

Hydra:,

WWW Link. Real-time system to build 3D scene graphs from sensor data. Code, Scene Graph. Code, RGB-D.

Jain, H.[Hardik], Wöllhaf, M., Hellwich, O.[Olaf],
Learning to Reconstruct Symmetric Shapes using Planar Parameterization of 3D Surface,
GMDL19(4133-4140)
IEEE DOI
Code, Symmetry.
WWW Link. Gaussian processes, geometry, image reconstruction, image representation, iterative methods, ShapeNet

Wang, K.[Kehan], Zheng, J.[Jia], Zhou, Z.[Zihan],
Neural Face Identification in a 2D Wireframe Projection of a Manifold Object,
CVPR22(1612-1621)
IEEE DOI
Code, CAD.
WWW Link. Manifolds, Solid modeling, Surface reconstruction, Design automation, Predictive models, Transformers, grouping and shape analysis

Sclaroff, S.[Stan], Isidoro, J.[John],
Active blobs: region-based, deformable appearance models,
CVIU(89), No. 2-3, February-March 2003, pp. 197-225.
Elsevier DOI

Earlier:
Active Blobs,
ICCV98(1146-1153).
IEEE DOI Code, Active Blobs.
HTML Version. Shape plus color texture map.

Torresani, L.[Lorenzo], Hertzmann, A.[Aaron], Bregler, C.[Chris],
Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors,
PAMI(30), No. 5, May 2008, pp. 878-892.
IEEE DOI

Earlier: A1, A2, Only:
Automatic Non-rigid 3D Modeling from Video,
ECCV04(Vol II: 299-312).
Springer DOI
Given initial region, track and model non-rigid shape. Code, Structure from Motion. Code:
WWW Link. Earilier location:
WWW Link.

Hoover, A.[Adam], Goldgof, D.[Dmitry], Bowyer, K.W.[Kevin W.],
The Space Envelope: A Representation for 3D Scenes,
CVIU(69), No. 3, March 1998, pp. 310-329.
DOI Link Code, Space Envelope.
HTML Version.

CGAL: Computational Geometry Algorithms Library,
2016 Code, Computational Geometry.
WWW Link.

Lv, K.[Kai], Sheng, H.[Hao], Xiong, Z.[Zhang], Li, W.[Wei], Zheng, L.[Liang],
Pose-Based View Synthesis for Vehicles: A Perspective Aware Method,
IP(29), 2020, pp. 5163-5174.
IEEE DOI
Code, View Syntheses.
WWW Link. Solid modeling, Generative adversarial networks, Licenses, Cameras, Task analysis, vehicle pose

Liu, W., Piao, Z., Min, J., Luo, W., Ma, L., Gao, S.,
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis,
ICCV19(5903-5912)
IEEE DOI
Code, Convolutional Neural Networks.
HTML Version. convolutional neural nets, feature extraction, image denoising, image motion analysis, image sequences, pose estimation, Face

restoreInpaint,
2000.
WWW Link. Code, Restoration. Code, Inpainting. filling detected cracks and missing thin parts of images, paintings, frescos, removing noise, enhancing brightness, color and details, etc.

Getreuer, P.[Pascal],
Total Variation Inpainting Using Split Bregman,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Inpainting.
See also Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman.
See also Nontexture Inpainting by Curvature-Driven Diffusions.

Galerne, B.[Bruno], Leclaire, A.[Arthur],
Texture Inpainting Using Efficient Gaussian Conditional Simulation,
SIIMS(10), No. 3, 2017, pp. 1446-1474.
DOI Link

And:
An Algorithm for Gaussian Texture Inpainting,
IPOL(7), 2017, pp. 262-277.
DOI Link
Code, Inpainting.

Liu, H., Jiang, B., Xiao, Y., Yang, C.,
Coherent Semantic Attention for Image Inpainting,
ICCV19(4169-4178)
IEEE DOI
Code, Inpainting.
WWW Link. feature extraction, image coding, image recognition, image restoration, image segmentation, image texture, Painting

El-Nouby, A., Sharma, S., Schulz, H., Hjelm, R.D., Asri, L.E., Kahou, S.E., Bengio, Y., Taylor, G.,
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction,
ICCV19(10303-10311)
IEEE DOI
Code, Image Editing.
WWW Link. computational linguistics, image processing, recurrent neural nets, text analysis, one-step generation tasks,

di Martino, J.M.[J. Matías], Facciolo, G.[Gabriele], Meinhardt-Llopis, E.[Enric],
Poisson Image Editing,
IPOL(6), 2016, pp. 300-325.
DOI Link
Code, Poisson. Gradient based image processing.
See also Poisson image editing.
See also Fourier implementation of Poisson image editing.

Lu, H., Dai, Y., Shen, C., Xu, S.,
Indices Matter: Learning to Index for Deep Image Matting,
ICCV19(3265-3274)
IEEE DOI
Code, Matting.
WWW Link. convolutional neural nets, decoding, feature extraction, image colour analysis, image enhancement, image sampling, Deconvolution

Make3D,
Online2009.
WWW Link. Code, 3D Fly Through. 3-D fly through from a single image.

Fedorov, V.[Vadim], Facciolo, G.[Gabriele], Arias, P.[Pablo],
Variational Framework for Non-Local Inpainting,
IPOL(5), 2015, pp. 362-386.
DOI Link
Code, Inpainting.
See also Variational Framework for Exemplar-Based Image Inpainting, A.

Xie, C., Liu, S., Li, C., Cheng, M., Zuo, W., Liu, X., Wen, S., Ding, E.,
Image Inpainting With Learnable Bidirectional Attention Maps,
ICCV19(8857-8866)
IEEE DOI
Code, Inpainting.
WWW Link. convolutional neural nets, feature extraction, image colour analysis, image restoration, Image reconstruction

Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.,
Free-Form Image Inpainting With Gated Convolution,
ICCV19(4470-4479)
IEEE DOI
Code, Inpainting.
WWW Link. computational geometry, convolutional neural nets, feature extraction, feature selection, image restoration, Training

Li, J., He, F., Zhang, L., Du, B., Tao, D.,
Progressive Reconstruction of Visual Structure for Image Inpainting,
ICCV19(5961-5970)
IEEE DOI
Code, Inpainting.
WWW Link. convolutional neural nets, image restoration, neural net architecture, corrupted images, Shape

Sterzentsenko, V., Saroglou, L., Chatzitofis, A., Thermos, S., Zioulis, N., Doumanoglou, A., Zarpalas, D., Daras, P.,
Self-Supervised Deep Depth Denoising,
ICCV19(1242-1251)
IEEE DOI
Code, Depth Denoising.
WWW Link. cameras, convolutional neural nets, data acquisition, image colour analysis, image denoising, Color

Kim, D.[Dahun], Woo, S.[Sanghyun], Lee, J.Y.[Joon-Young], Kweon, I.S.[In So],
Recurrent Temporal Aggregation Framework for Deep Video Inpainting,
PAMI(42), No. 5, May 2020, pp. 1038-1052.
IEEE DOI
Code, Inpainting.
WWW Link.
WWW Link. Video inpainting, video completion, video object removal, video caption removal, video decaptioning, video editing

Liu, M., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., Kautz, J.,
Few-Shot Unsupervised Image-to-Image Translation,
ICCV19(10550-10559)
IEEE DOI
Code, Registration.
WWW Link. image registration, unsupervised learning, few-shot unsupervised image-to-image translation methods, Convolutional codes

Yoo, J., Uh, Y., Chun, S., Kang, B., Ha, J.,
Photorealistic Style Transfer via Wavelet Transforms,
ICCV19(9035-9044)
IEEE DOI
Code, Style Transfer.
WWW Link. feature extraction, image colour analysis, image representation, realistic images, video signal processing, wavelet transforms, Image reconstruction

Nayar, S.N., Belhumeur, P.N., and Boult, T.E.,
Lighting Sensitive Display,
ToG(23), No. 4, October 2004, pp. 963-979.
PDF File. Code, Relighting.
WWW Link.

Chen, Z.Y.[Zhuo-Yuan], Guo, D.[Demi], Xiao, T.[Tong], Xie, S.N.[Sai-Ning], Chen, X.L.[Xin-Lei], Szlam, A.[Arthur], Tulsiani, S.[Shubham], Yu, H.N.[Hao-Nan], Gray, J.[Jonathan], Srinet, K.[Kavya], Qi, C.R.[Charles R.], Fan, H.Q.[Hao-Qi], Ma, J.[Jerry], Zitnick, L.[Larry],
Order-Aware Generative Modeling Using the 3D-Craft Dataset,
ICCV19(1764-1773)
IEEE DOI
Code, 3D.
WWW Link. Prediction of ordered actions to construct 3D objects by watching human actions. computer games, learning (artificial intelligence), solid modelling, human action sequences, Computational modeling

Siddiquee, M.M.R., Zhou, Z., Tajbakhsh, N., Feng, R., Gotway, M., Bengio, Y., Liang, J.,
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization,
ICCV19(191-200)
IEEE DOI
Code, Generative Adversarial Networks.
WWW Link. diseases, image classification, learning (artificial intelligence), medical image processing, Face

Dollar, P.,
Structured edge detection toolbox,
WWW Link. Code, Edge Detection.

Iverson, L.A., Zucker, S.W.,
Logical/Linear Operators for Image Curves,
PAMI(17), No. 10, October 1995, pp. 982-996.
IEEE DOI Code, Edge Detection. Unify aspects of Linear operator theory and boolean logic. Code is available through the paper pointer:
HTML Version. Compared in:
See also Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms, A.

Smith, S.M.[Stephen M.], Brady, J.M.,
Susan: A New Approach to Low-Level Image-Processing,
IJCV(23), No. 1, May 1997, pp. 45-78.
DOI Link

Earlier: Defence Research AgencyUK, TR95SMS1, 1995. Code, Edge Detection. Code:
HTML Version.

von Gioi, R.G.[Rafael Grompone], Randall, G.[Gregory],
Unsupervised Smooth Contour Detection,
IPOL(6), 2016, pp. 233-267.
DOI Link
Code, Edge Detection. similar to Marr-Hildreth (
See also Theory of Edge Detection. ), heuristics from Canny (
See also Computational Approach to Edge Detection, A. ) and Devernay (
See also Non-Maximal Suppression Method for Edge Detection with Sub-Pixel Accuracy, A. )

Baker, S.[Simon], Nayar, S.K.[Shree K.],
Global Measures of Coherence for Edge Detector Evaluation,
CVPR99(II: 373-379).
IEEE Abstract. Code, Edge Detection.
IEEE DOI For code and images:
WWW Link.

von Gioi, R.G.[Rafael Grompone], Jakubowicz, J.[Jeremie], Morel, J.M.[Jean-Michel], Randall, G.[Gregory],
LSD: a Line Segment Detector,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Line Segments.

Rajaei, B.[Boshra], von Gioi, R.G.[Rafael Grompone],
Gestaltic Grouping of Line Segments,
IPOL(8), 2018, pp. 37-50.
DOI Link
Code, Line Segments.

Rosin, P.L.[Paul L.], West, G.A.W.[Geoff A.W.],
Nonparametric Segmentation of Curves into Various Representations,
PAMI(17), No. 12, December 1995, pp. 1140-1153.
IEEE DOI Code, Curve Segmentation. (Code is available:
WWW Link.
Detection of Circular Arcs in Images,
Alvey88(259-263).
Earlier: A2, A1:
Multi-stage Combined Ellipse and Line Detection,
BMVC92(197-206).
PDF File.
Segments into various components, lines, arcs (circular, elliptical, etc.). A fairly general complete algorithm. An extensive bibliography of earlier curve partitioning work.

Rosin, P.L.[Paul L.],
Non-Parametric Multi-Scale Curve Smoothing,
PRAI(8), 1994, pp. 1381-1406.
Earlier: SPIE(1964), April 1993, pp. 66-77 Code, Curve Smoothing. Code is available:
WWW Link.

Provot, L.[Laurent], Gérard, Y.[Yan], Feschet, F.[Fabien],
Digital Level Layers for Digital Curve Decomposition and Vectorization,
IPOL(2014), No. 1, pp. 169-186.
DOI Link
Code, Curve Decomposition.
Earlier: A2, A1, A3:
Introduction to Digital Level Layers,
DGCI11(83-94).
Springer DOI

He, Y.C.[Yu-Chen], Kang, S.H.[Sung Ha], Morel, J.M.[Jean-Michel],
Binary Shape Vectorization by Affine Scale-space,
IPOL(13), 2023, pp. 22--37.
DOI Link
Code, Vectorization.

Sivignon, I.[Isabelle],
A Near-Linear Time Guaranteed Algorithm for Digital Curve Simplification Under the Fréchet Distance,
IPOL(2014), No. 2014, pp. 116-127.
DOI Link
Code, Curve Partitions. DGCI11(333-345).
Springer DOI

Zingaretti, P.[Primo], Gasparroni, M.[Massimiliano], Vecci, L.[Lorenzo],
Fast Chain Coding of Region Boundaries,
PAMI(20), No. 4, April 1998, pp. 407-415.
IEEE DOI
Code, Chain Code. Single pass algorithm to convert from raster to chain codes. Detailed code in the paper.

Chain Code Representation,
2007.
WWW Link. Code, Chain Code. Code, Chain Code, C.

Chernov, N.[Nikolai],
Circular and Linear Regression: Fitting Circles and Lines by Least Squares,
CRC PressBoca Raton, FL, June 22, 2010 ISBN: 9781439835906.
WWW Link. Buy this book: Circular and Linear Regression: Fitting Circles and Lines by Least Squares (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Code, Image Processing, Matlab. Survey, Circle Fitting.

Pilu, M.[Maurizio], Fitzgibbon, A.W., Fisher, R.B.,
Ellipse-Specific Direct Least-Square Fitting,
ICIP96(III: 599-602).
IEEE DOI
And: DAINo. 806, May 1996. EdinburghDirectly solved by a generalized eigen-system. Includes Matlab code. Code, Ellipse Fitting.

Kanatani, K.[Kenichi], Sugaya, Y.[Yasuyuki], Kanazawa, Y.S.[Yasu-Shi],
Ellipse Fitting for Computer Vision: Implementation and Applications,
Morgan & ClaypoolPublishers, San Rafael, CA, U.S., April, 2016. ISBN: 9781627054584.
DOI Link
Code, Ellipse Fitting.
WWW Link. Algebraic Fitting, Geometric Fitting, Robust Fitting, Ellipse-based 3-D Computation, Experiments and Examples, Extension and Generalization, Accuracy of Algebraic Fitting, Maximum Likelihood and Geometric Fitting, Theoretical Accuracy Limit

Harris, C., Stephens, M.J.,
A Combined Corner and Edge Detector,
Alvey88(147-152). Code, Edge Detection.
DOI Link
PDF File.
See also Analysis and Implementation of the Harris Corner Detector, An.

Buades, A.[Antonio], von Gioi, R.G.[Rafael Grompone], Navarro, J.[Julia],
Joint Contours, Corner and T-Junction Detection: An Approach Inspired by the Mammal Visual System,
JMIV(60), No. 3, March 2018, pp. 341-354.
Springer DOI

And:
Contours, Corners and T-Junctions Detection Algorithm,
IPOL(8), 2018, pp. 24-36.
DOI Link
Code, Edge Detection.

Hough Transform Code,
2007.
WWW Link. Code, Hough Transform. Code, Hough Transform, C.

Dalitz, C.[Christoph], Schramke, T.[Tilman], Jeltsch, M.[Manuel],
Iterative Hough Transform for Line Detection in 3D Point Clouds,
IPOL(7), 2017, pp. 184-196.
DOI Link
Code, Hough Transform. Code, Line Detection.
See also TriplClust: An Algorithm for Curve Detection in 3D Point Clouds.

Monga, O.[Oliver], Deriche, R.[Rachid], Malandain, G.[Grégoire], Cocquerez, J.P.[Jean Pierre],
Recursive Filtering and Edge Tracking: Two Primary Tools for 3D Edge Detection,
IVC(9), No. 4, August 1991, pp. 203-214.
Elsevier DOI Code, Edge Detection.
Earlier:
3D Edge Detection by Separable Recursive Filtering and Edge Closing,
ICPR90(I: 652-654).
IEEE DOI
And:
Recursive Filtering and Edge Closing: Two Primary Tools for 3D Edge Detection,
ECCV90(56-65).
Springer DOI For the code see:
WWW Link. See the above paper.
See also Thin Nets and Crest Lines: Application to Satellite Data and Medical Images.

Digne, J.[Julie], de Franchis, C.[Carlo],
The Bilateral Filter for Point Clouds,
IPOL(7), 2017, pp. 278-287.
DOI Link
Code, Bilateral Filter. For 3D data.

Kalman Filter Library,
January, 2006.
WWW Link. Code, Kalman Filter.

Welch, G.[Greg], and Bishop, G.[Gary],
An Introduction to the Kalman Filter,
TR95-041, University of North Carolina at Chapel Hill, Department of Computer Science, 1995.
WWW Link. Survey, Kalman Filter. Code, Kalman Filter. Tutorial on Kalman filter. All you want to know.

Simoncelli, E.P.[Eero P.], and Freeman, W.T.[William T.],
The Steerable Pyramid: A Flexible Architecture for Multi-Scale Derivative Computation,
ICIP95(III: 444-447).
IEEE DOI
Steerable Filter. Code, Steerable Filter. tight frame, rotation-invariant filters.
HTML Version. And
PS File. Code is also available:
HTML Version.

Briand, T.[Thibaud], Vacher, J.[Jonathan],
How to Apply a Filter Defined in the Frequency Domain by a Continuous Function,
IPOL(6), 2016, pp. 183-211.
DOI Link
Code, Filters. Filter real valued images, filter is a continuous function.

Li, Y., Gu, S., Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Learning Filter Basis for Convolutional Neural Network Compression,
ICCV19(5622-5631)
IEEE DOI
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, image classification, image filtering, image resolution, Neural networks

Monasse, P.[Pascal],
Quasi-Euclidean Epipolar Rectification,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Rectification.

Oram, D.,
Rectification for any epipolar geometry,
BMVC01(Session 7: Geometry &. Structure).
HTML Version.
HTML Version. Code, Rectification. Code:
WWW Link. University of Manchester

Wang, W., Guo, R., Tian, Y., Yang, W.,
CFSNet: Toward a Controllable Feature Space for Image Restoration,
ICCV19(4139-4148)
IEEE DOI
Code, Image Restoration.
WWW Link. image reconstruction, image resolution, image restoration, learning (artificial intelligence), Distortion

Cha, S., Moon, T.,
Fully Convolutional Pixel Adaptive Image Denoiser,
ICCV19(4159-4168)
IEEE DOI
Code, Denoising.
WWW Link. convolutional neural nets, image denoising, Robustness

Buades, A.[Antoni], Coll, B.[Bartomeu], Morel, J.M.[Jean-Michel],
Non-local Means Denoising,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Denoising.
See also review of image denoising algorithms, with a new one, A.
See also Chambolle's Projection Algorithm for Total Variation Denoising.
See also Nonlocal Image and Movie Denoising.

Delon, J.[Julie], Desolneux, A.[Agnès], Guillemot, T.[Thierry],
PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images,
IPOL(6), 2016, pp. 130-154.
DOI Link
Code, Impulse Noise.

Hurault, S.[Samuel], Ehret, T.[Thibaud], Arias, P.[Pablo],
EPLL: An Image Denoising Method Using a Gaussian Mixture Model Learned on a Large Set of Patches,
IPOL(8), 2018, pp. 465-489.
DOI Link
Code, Noise Removal.
See also From learning models of natural image patches to whole image restoration.

Colom, M.[Miguel], Buades, A.[Antoni],
Analysis and Extension of the Percentile Method, Estimating a Noise Curve from a Single Image,
IPOL(2013), No. 2013, pp. 332-359.
DOI Link
Code, Denoising.

Colom, M.[Miguel], Buades, A.[Antoni],
Analysis and Extension of the PCA Method, Estimating a Noise Curve from a Single Image,
IPOL(6), 2016, pp. 365-390.
DOI Link
Code, Denoising.

Lebrun, M.[Marc], Colom, M.[Miguel], Morel, J.M.[Jean-Michel],
The Noise Clinic: a Blind Image Denoising Algorithm,
IPOL(5), 2015, pp. 1-54.
DOI Link
Code, Denoising.
Earlier:
The noise clinic: A universal blind denoising algorithm,
ICIP14(2674-2678)
IEEE DOI
Covariance matrices

Verdoja, F.[Francesco], Grangetto, M.[Marco],
Graph Laplacian for image anomaly detection,
MVA(31), No. 1, January 2020, pp. Article 11.
Springer DOI
Code, Anomaly Detection.
WWW Link. Reed-Xiaoli detector

Tassano, M.[Matias], Delon, J.[Julie], Veit, T.[Thomas],
An Analysis and Implementation of the FFDNet Image Denoising Method,
IPOL(9), 2019, pp. 1-25.
DOI Link
Code, Noise Removal.
See also FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising.

Ehret, T.[Thibaud], Davy, A.[Axel], Delbracio, M.[Mauricio], Morel, J.M.[Jean-Michel],
How to Reduce Anomaly Detection in Images to Anomaly Detection in Noise,
IPOL(9), 2019, pp. 391-412.
DOI Link
Code, Anomaly Detection.

di Martino, M.[Matías], Facciolo, G.[Gabriele],
An Analysis and Implementation of Multigrid Poisson Solvers With Verified Linear Complexity,
IPOL(8), 2018, pp. 192-218.
DOI Link
Code, Poisson Solver.

Duran, J.[Joan], Coll, B.[Bartomeu], Sbert, C.[Catalina],
Chambolle's Projection Algorithm for Total Variation Denoising,
IPOL(2013), No. 2013, pp. 311-331.
DOI Link
Code, Total Variation. Code, Denoising.
See also Algorithm for Total Variation Minimization and Applications, An.
See also Nonlocal Image and Movie Denoising.

Delbracio, M.[Mauricio], Almansa, A.[Andrés], Musé, P.[Pablo],
Recovering the Subpixel PSF from Two Photographs at Different Distances,
IPOL(2013), No. 2013, pp. 232-241.
DOI Link
Code, Point Spread Function.
See also Subpixel Point Spread Function Estimation from Two Photographs at Different Distances.

Delbracio, M.[Mauricio], Musé, P.[Pablo], Almansa, A.[Andrés],
Non-parametric sub-pixel local point spread function estimation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, PSF Estimation.

Zhao, H., Shao, W., Bao, B., Li, H.,
A Simple and Robust Deep Convolutional Approach to Blind Image Denoising,
CLI19(3943-3951)
IEEE DOI
Code, Convolutional Networks.
WWW Link. AWGN, convolutional neural nets, feature extraction, Gaussian noise, image denoising, noise estimation

Lebrun, M.[Marc],
An Analysis and Implementation of the BM3D Image Denoising Method, Image Processing,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Denoising.
See also Small Neural Networks can Denoise Image Textures Well: A Useful Complement to BM3D.

Ehret, T.[Thibaud], Arias, P.[Pablo],
Implementation of VBM3D and Some Variants,
IPOL(11), 2021, pp. 374-395.
DOI Link
Code, Viedo Denoise. VBM3D is an extension to video of the well-known image denoising algorithm BMD3.
See also Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering. o

Ayzik, S.[Sharon], Avidan, S.[Shai],
Deep Image Compression Using Decoder Side Information,
ECCV20(XVII:699-714).
Springer DOI
Code, Compression.
WWW Link. Information available only to the decoder. Learn the transformation.

Section, Multiple Entries: 5.4.3.2 Vector Quantization Code Book Generation, Learning and Search Chapter Contents (Back)
Vector Quantization. VQ Codebook.

JPEG: Joint Photographic Experts Group,
Online2007.
WWW Link. Society, JPEG. Society, Image Analysis. Code, Image Processing. The JPEG standards committee. Code, standards, etc.

Nikoukhah, T.[Tina ], Anger, J.[Jérémy], Colom, M.[Miguel], Morel, J.M.[Jean-Michel], von Gioi, R.G.[Rafael Grompone],
ZERO: a Local JPEG Grid Origin Detector Based on the Number of DCT Zeros and its Applications in Image Forensics,
IPOL(11), 2021, pp. 396-433.
DOI Link
Code, JPEG Analysis. A statistical test, based on Desolneux, Moisan and Morel's a contrario theory, is used to decide when a significant JPEG grid.
See also From Gestalt Theory to Image Analysis: A Probabilistic Approach.

Nikoukhah, T.[Tina], Colom, M.[Miguel], Morel, J.M.[Jean-Michel], von Gioi, R.G.[Rafael Grompone],
A Reliable JPEG Quantization Table Estimator,
IPOL(12), 2022, pp. 173-197.
DOI Link
Code, JPEG. Code, Forensics, JPEG.
See also Identification of Bitmap Compression History: JPEG Detection and Quantizer Estimation.

JPEG 2000,
Code, Image Processing.
HTML Version. Survey, JPEG. The standards organization page for JPEG 2000.

Wang, Z.W.[Zi-Wei], Lu, J.W.[Ji-Wen], Xiao, H.[Han], Liu, S.Y.[Sheng-Yu], Zhou, J.[Jie],
Learning Accurate Performance Predictors for Ultrafast Automated Model Compression,
IJCV(131), No. 7, July 2023, pp. 1761-1783.
Springer DOI
Code, Model Compression.
WWW Link.

MPEG Org Home Page,
2007.
WWW Link. Code, Image Analysis. A family of standards used for coding audio-visual information (e.g., movies, video, music) in a digital compressed format. Focuses mostly on MPEG-1 and MPEG-2 standards and products.

Liu, D.Y., Tsai, C.W., Wu, J.L.,
A Java-Based MPEG-4 Like Video Codec,
Consumer(44), No. 1, February 1998, pp. 200-205.
Code, Image Coding. Code, Image Coding, Java.

Sühring, K.,
H.264/AVC Refrence Software,
Online Book2005.
WWW Link. Code, H.264/AVC.

Section, Multiple Entries: 5.5.10 Coded Aperture Compressive Sensing Chapter Contents (Back)
Compressive Sensing. Compressed Sensing. Coded Aperture.
See also Light Field Cameras, Images, and Analysis.

Tendero, Y.[Yohann], Landeau, S.[Stephane], Gilles, J.[Jerome],
Non-uniformity Correction of Infrared Images by Midway Equalization,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Equalization. infrared equalization

Lisani, J.L.[Jose-Luis],
Local Contrast Enhancement based on Adaptive Logarithmic Mappings,
IPOL(10), 2020, pp. 43-61.
DOI Link
Code, Contrast Enhancement.

Lisani, J.L.[Jose-Luis],
An Analysis and Implementation of the Shape Preserving Local Histogram Modification Algorithm,
IPOL(8), 2018, pp. 408-434.
DOI Link
Code, Histogram Modification.

Guillemot, T.[Thierry], Delon, J.[Julie],
Implementation of the Midway Image Equalization,
IPOL(6), 2016, pp. 114-129.
DOI Link
Code, Image Equalization.
See also Midway Image Equalization.

Lisani, J.L.[Jose-Luis], Buades, A.[Antoni], Morel, J.M.[Jean-Michel],
Image Color Cube Dimensional Filtering and Visualization,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Color Histograms.

Getreuer, P.[Pascal],
Automatic Color Enhancement (ACE) and its Fast Implementation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Color Enhancement.

Image Processing Online,
IPOL( Vol No. ), WWW Link.
Open journal, open source. Code for algorithms. Code, Image Processing.

Lotus Hill Institute,
Imageparsing WWW Link.
Research Group, China. Dataset, Segmentation. Code, Viewing. The Imageparsing site is devoted to providing ground truth datasets and Matlab code for annotation and viewing.
See also LHI Object Datasets.
See also LHI Sports Activity Dataset.
See also LHI Segmentation Dataset.
See also LHI Surveillance Dataset.

Khoral Research, Inc,
Software development. WWW Link.
Creators of Khoros which is now available from: AccuSoft,
WWW Link.
Code, Image Processing. Research Group, Company.

National Institute of Standards and Technology (NIST) Intelligent Systems Division,
NISTIR WWW Link.
Multimodal Information Group WWW Link.

See also Multimedia Event Detection. WWW Link.
Earlier references: Journal of Research National Bureau of Standards,
NBS( Vol No. ), NIST Guide to Available Mathematical Software,
Software guide. WWW Link.
Code, Mathematical Software. Research Group, US Government.
See also FERET Database, The.
See also NIST Mugshot Identification Database.
See also NIST ICE Iris Image Database.
See also NIST Special Database 4, Fingerprint Database.
See also TRECVID Workshop DAta.

Robot Vision 2 Inc.,
Image processing. WWW Link.
Research Group, Company. Code, Image Analysis. Cross Platform software development and software for Image Processing and Robot Vision

Schuck, P.W.[Peter W.],
Tracking Vector Magnetograms with the Magnetic Induction Equation,
ApJ(683), 2008, pp. 1134-1152
DOI Link A variant of the Lucas-Kanade Motion estimation algorithm IDL code available with the paper free version.
WWW Link. Code, Tracking.

Atkinson, D.[Devan], Becker, T.[Thorsten],
A 117 Line 2D Digital Image Correlation Code Written in MATLAB,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link
Code, Correlation.
And: Correction: RS(13), No. 10, 2021, pp. xx-yy.
DOI Link

Felzenszwalb, P.F.[Pedro F.], Huttenlocher, D.P.[Daniel P.],
Efficient Belief Propagation for Early Vision,
IJCV(70), No. 1, October 2006, pp. 41-54.
Springer DOI
PDF File.

Earlier: CVPR04(I: 261-268).
IEEE DOI
PDF File. Code, Stereo. Code:
WWW Link. For stereo and reconstruction.

Crandall, D.J.[David J.], Huttenlocher, D.P.[Daniel P.],
Composite Models of Objects and Scenes for Category Recognition,
CVPR07(1-8).
IEEE DOI

Earlier:
Weakly Supervised Learning of Part-Based Spatial Models for Visual Object Recognition,
ECCV06(I: 16-29).
Springer DOI
Code, Object Recognition. Code:
WWW Link.

Crandall, D.J.[David J.], Felzenszwalb, P.F.[Pedro F.], Huttenlocher, D.P.[Daniel P.],
Spatial Priors for Part-Based Recognition Using Statistical Models,
CVPR05(I: 10-17).
IEEE DOI
PDF File.
Code, Object Recognition. Code:
WWW Link. Find matches for the parts in an overall structure.

Rajwade, A.[Ajit], Banerjee, A.[Arunava], Rangarajan, A.[Anand],
Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration,
PAMI(31), No. 3, March 2009, pp. 475-491.
IEEE DOI
Code, Isocontour.
Earlier:
New Method of Probability Density Estimation with Application to Mutual Information Based Image Registration,
CVPR06(II: 1769-1776).
IEEE DOI
Assume an image is a piecewise-continuous function, not a discrete set of pixels. Code is available.
HTML Version.

Moisan, L.[Lionel], Moulon, P.[Pierre], Monasse, P.[Pascal],
Automatic Homographic Registration of a Pair of Images, with A Contrario Elimination of Outliers,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Image Registration.
See also Fundamental Matrix of a Stereo Pair, with A Contrario Elimination of Outliers.

Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.,
elastix: A Toolbox for Intensity-Based Medical Image Registration,
MedImg(29), No. 1, January 2010, pp. 196-205.
IEEE DOI
Code, Registration.

Yu, G.S.[Guo-Shen], Morel, J.M.[Jean-Michel],
ASIFT: An Algorithm for Fully Affine Invariant Comparison,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, SIFT.

Farhan, E.[Erez], Meir, E.[Elad], Hagege, R.R.[Rami R.],
Local Region Expansion: A Method for Analyzing and Refining Image Matches,
IPOL(7), 2017, pp. 386-398.
DOI Link
Code, Matching.

Farhan, E.[Erez],
Matching of Weakly-Localized Features under Different Geometric Models,
IPOL(10), 2020, pp. 1-23.
DOI Link
Code, Matching. Region Matching.

Briand, T.[Thibaud], Facciolo, G.[Gabriele], Sánchez, J.[Javier],
Improvements of the Inverse Compositional Algorithm for Parametric Motion Estimation,
IPOL(8), 2018, pp. 435-464.
DOI Link
Code, Registration.
See also Equivalence and Efficiency of Image Alignment Algorithms.
See also Pyramid Approach to Subpixel Registration Based on Intensity, A.

Zhou, F., de la Torre, F.[Fernando],
Generalized Canonical Time Warping,
PAMI(38), No. 2, February 2016, pp. 279-294.
IEEE DOI
Code, Time Warping. For animation. Temporal alignment of human motion. Complexity theory The code is available:
WWW Link.

Hess, R.[Robin],
SIFT Feature Detector,
2008 Code, SIFT.
WWW Link.

Jiang, Q., He, Y., Li, G., Lin, J., Li, L., Li, W.,
SVD: A Large-Scale Short Video Dataset for Near-Duplicate Video Retrieval,
ICCV19(5280-5288)
IEEE DOI
Code, SVD.
WWW Link. data mining, feature extraction, image matching, image representation, indexing, pattern clustering, Feature extraction

Pistonesi, S.[Silvina], Martinez, J.[Jorge], Ojeda, S.M.[Silvia Maria], Vallejos, R.[Ronny],
Structural Similarity Metrics for Quality Image Fusion Assessment: Algorithms,
IPOL(8), 2018, pp. 345-368.
DOI Link
Code, Fusion. Code, Fusion, Matlab.

Scheffler, D.[Daniel], Hollstein, A.[André], Diedrich, H.[Hannes], Segl, K.[Karl], Hostert, P.[Patrick],
AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link
Code, Registration.

Buades, A.[Antoni], Coll, B.[Bartomeu], Duran, J.[Joan], Sbert, C.[Catalina],
Implementation of Nonlocal Pansharpening Image Fusion,
IPOL(2014), No. 2014, pp. 1-15.
DOI Link
Code, Pansharpening.
See also Nonlocal Variational Model for Pansharpening Image Fusion, A.

Huang, Q.B.[Qing-Bao], Liang, Y.[Yu], Wei, J.L.[Jie-Long], Cai, Y.[Yi], Liang, H.Y.[Han-Yu], Leung, H.F.[Ho-Fung], Li, Q.[Qing],
Image Difference Captioning With Instance-Level Fine-Grained Feature Representation,
MultMed(24), No. 2022, pp. 2004-2017.
IEEE DOI
WWW Link. Code, Change Detection. Feature extraction, Semantics, Visualization, Task analysis, Image color analysis, Proposals, Interference, similarity-based difference finding

Dagobert, T.[Tristan], Grompone-von Gioi, R.[Rafael], de Franchis, C.[Carlo], Hessel, C.[Charles],
Detection and Interpretation of Change in Registered Satellite Image Time Series,
IPOL(12), 2022, pp. 625-651.
DOI Link
Code, Change Detection.

Gupta, K., Petersson, L., Hartley, R.I.,
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation,
R6D19(2758-2766)
IEEE DOI
Code, Pose Estimation.
WWW Link. convolutional neural nets, object detection, pose estimation, pose aware confidence scores, object pose estimation, CullNet

Lee, J.J.[John J.],
LIBPMK: A Pyramid Match Toolkit,
CSAIL-2008-017, April 2008.
WWW Link. Code, Matching. Code for:
See also Pyramid Match Hashing: Sub-Linear Time Indexing Over Partial Correspondences.

Rodríguez, M.[Mariano], Facciolo, G.[Gabriele], von Gioi, R.G.[R. Grompone], Musé, P., Delon, J., Morel, J.M.,
CNN-Assisted Coverings in the Space of Tilts: Best Affine Invariant Performances with the Speed of CNNs,
ICIP20(2201-2205)
IEEE DOI
Code, Affine Invariant.
WWW Link. Cameras, Adaptation models, Image matching, Mathematical model, Estimation, Optical imaging, Distortion, image comparison, convolutional neural networks

Persoon, E., and Fu, K.S.,
Shape Discrimination Using Fourier Descriptors,
SMC(7), No. 3, March 1977, pp. 170-179.
And: Reprinted: PAMI(8), No. 3, May 1986, pp. 388-397.
Earlier: ICPR74(126-130). Code, Fourier.
HTML Version.

Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.,
Moment Matching for Multi-Source Domain Adaptation,
ICCV19(1406-1415)
IEEE DOI
Code, Moments.
WWW Link. learning (artificial intelligence), neural nets, object recognition, multisource UDA, moment matching, Data models

Sclaroff, S.[Stan], and Pentland, A.P.,
Modal Matching for Correspondence and Recognition,
PAMI(17), No. 6, June 1995, pp. 545-561.
IEEE DOI
And: Vismod-304, 1994.
HTML Version. and
PS File. Code, Matching. Gelerkin Approximation. Finite Element Analysis. Applies to matching 2-D contours and points. Similar to the Proximity Matrix. formulations.
See also Closed-Form Solutions for Physically Based Shape Modeling and Recognition.
See also Modal Matching: A Method for Describing, Comparing, and Manipulating Digital Signals.

Vedaldi, A.[Andrea], and Fulkerson, B.[Brian],
VLFeat,
2005 Code, SIFT. Award, PAMI Everingham, 2015.
WWW Link.

VripPack: Volumetric Range Image Processing Package,
Online2006.
WWW Link. Code, Range Registration.

TEASER++: Certifiable 3D Registration,
WWW Link. Code, Point Cloud Registration.
A fast and robust point-cloud registration library. From the papers:
See also TEASER: Fast and Certifiable Point Cloud Registration.

Wang, J.S.[Jia-Shun], Wen, C.[Chao], Fu, Y.W.[Yan-Wei], Lin, H.T.[Hai-Tao], Zou, T.Y.[Tian-Yun], Xue, X.Y.[Xiang-Yang], Zhang, Y.D.[Yin-Da],
Neural Pose Transfer by Spatially Adaptive Instance Normalization,
CVPR20(5830-5838)
IEEE DOI
Code, Mesh Pose.
WWW Link. Shape, Feature extraction, Strain, Decoding, Machine learning, Task analysis

Hodan, T.[Tomáš], Matas, J.G.[Jirí G.], Obdržálek, Š.[Štepán],
On Evaluation of 6D Object Pose Estimation,
6DPose16(III: 606-619).
Springer DOI
Code, Pose Estimation.
WWW Link.

Bai, Y.T.[Yu-Tong], Liu, Q.[Qing], Xie, L.X.[Ling-Xi], Zheng, Y.[Yan], Qiu, W.C.[Wei-Chao], Yuille, A.L.[Alan L.],
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints From Limited Training Data,
ICCV19(7534-7544)
IEEE DOI
Code, Matching.
WWW Link. CAD, image matching, image representation, learning (artificial intelligence), object detection, Feature extraction

Chen, T.S.[Tian-Shui], Xu, M.X.[Mu-Xin], Hui, X.L.[Xiao-Lu], Wu, H.F.[He-Feng], Lin, L.A.[Li-Ang],
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition,
ICCV19(522-531)
IEEE DOI
Code, Graph Representation.
WWW Link. graph theory, image recognition, learning (artificial intelligence), semantic regions, Task analysis

Manandhar, D.[Dipu], Ruta, D.[Dan], Collomosse, J.[John],
Learning Structural Similarity of User Interface Layouts Using Graph Networks,
ECCV20(XXII:730-746).
Springer DOI
Code, Graph Matching.
WWW Link.

Torresani, L.[Lorenzo], Kolmogorov, V.[Vladimir], Rother, C.[Carsten],
A Dual Decomposition Approach to Feature Correspondence,
PAMI(35), No. 2, February 2013, pp. 259-271.
IEEE DOI

Earlier:
Feature Correspondence Via Graph Matching: Models and Global Optimization,
ECCV08(II: 596-609).
Springer DOI
PDF File.
Code, Matching. Code:
WWW Link.

Xu, M.H.[Ming-Hao], Wang, H.[Hang], Ni, B.B.[Bing-Bing], Tian, Q.[Qi], Zhang, W.J.[Wen-Jun],
Cross-Domain Detection via Graph-Induced Prototype Alignment,
CVPR20(12352-12361)
IEEE DOI
Code, Alignment.
WWW Link. Proposals, Prototypes, Task analysis, Detectors, Adaptation models, Training, Merging

Wu, T.[Tao], Ma, H.Y.[Hong-Yu], Wang, C.[Chao], Qiao, S.J.[Shao-Jie], Zhang, L.[Liang], Yu, S.[Shui],
Heterogeneous Representation Learning and Matching for Few-Shot Relation Prediction,
PR(131), 2022, pp. 108830.
Elsevier DOI
Code, Relations.
WWW Link. Knowledge graphs, Few-shot learning, Relation prediction, Representation learning, Convolutional network

Hu, R., Rohrbach, A., Darrell, T.J., Saenko, K.,
Language-Conditioned Graph Networks for Relational Reasoning,
ICCV19(10293-10302)
IEEE DOI
Code, Graph Representation.
WWW Link. feature extraction, graph theory, image representation, inference mechanisms, iterative methods, message passing, Knowledge discovery

Cai, Z.P.[Zhi-Peng], Chin, T., Koltun, V.,
Consensus Maximization Tree Search Revisited,
ICCV19(1637-1645)
IEEE DOI
Code, Search.
WWW Link. computational complexity, optimisation, tree searching, consensus maximization tree structure, Computational modeling

Komodakis, N.[Nikos], Tziritas, G.[Georgios], Paragios, N.[Nikos],
Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies,
CVIU(112), No. 1, October 2008, pp. 14-29.
Elsevier DOI

Earlier:
Fast, Approximately Optimal Solutions for Single and Dynamic MRFs,
CVPR07(1-8).
IEEE DOI
PDF File.
Code, Alignment.
WWW Link.
Earlier: A1, A3, A2:
MRF Optimization via Dual Decomposition: Message-Passing Revisited,
ICCV07(1-8).
IEEE DOI
Nonlinear programming techniques. Markov random fields; Linear programming; Primal-dual schema; Discrete optimization; Graph cuts

Ceres Solver,
2016.
WWW Link. Code, Optimization, C++.
Open source C++ library for modeling and solving large, complicated optimization problems.

Le, H.[Huu], Chin, T.J.[Tat-Jun], Eriksson, A.P.[Anders P.], Do, T.T.[Thanh-Toan], Suter, D.[David],
Deterministic Approximate Methods for Maximum Consensus Robust Fitting,
PAMI(43), No. 3, March 2021, pp. 842-857.
IEEE DOI
Code, Robust Fitting.
WWW Link. Approximation algorithms, Optimization, Mathematical model, Estimation, Computational modeling, approximate algorithm

Brédif, M., Tournaire, O.,
Librjmcmc: An Open-source Generic C++ Library For Stochastic Optimization,
ISPRS12(XXXIX-B3:259-264).
DOI Link
Code, Optimization.

Iwata, D.[Daichi], Waechter, M.[Michael], Lin, W.Y.[Wen-Yan], Matsushita, Y.[Yasuyuki],
An Analysis of Sketched IRLS for Accelerated Sparse Residual Regression,
ECCV20(XII: 609-626).
Springer DOI
Code, Least Squares.
WWW Link.

Meng, Z.[Zhaoyi], Merkurjev, E.[Ekaterina], Koniges, A.[Alice], Bertozzi, A.L.[Andrea L.],
Hyperspectral Image Classification Using Graph Clustering Methods,
IPOL(7), 2017, pp. 218-245.
DOI Link
Code, Hyperspectral Classification. Initial description:
See also Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme.
See also Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video. Parallel Implementation:
See also OpenMP parallelization and optimization of graph-based machine learning algorithms.

Murase, H.[Hiroshi], Nayar, S.K.[Shree K.], and Nene, S.A.[Sameer A.],
Software Library for Appearance Matching (SLAM),
ARPA94(I:733-737).
PDF File. Code, Matching.
WWW Link.

am_tools,
2007.
HTML Version. Code, Active Appearance Model. A set of tools to build and play with Appearance Models and AAMs.
See also University of Manchester, Medicine.

AAM Building,
February, 2007.
WWW Link. Code, Active Appearance Model. Code, Open Source.

Stegmann, M.B.[Mikkel B.],
Active Appearance Models,
Online2007.
WWW Link. Code, Active Appearance Model. Dataset, Active Appearance Model. AAM code and information.
See also Technical University of Denmark.

The FastICA package for MATLAB,
2007
WWW Link. Code, FastICA.

Lisani, J.L.[Jose-Luis], Morel, J.M.[Jean-Michel],
Exploring Patch Similarity in an Image,
IPOL(11), 2021, pp. 284-316.
DOI Link
Code, Matching. Compare using PCA
See also On lines and planes of closest fit to systems of points in space. or Gaussian mixture model
See also Maximum Likelihood from Incomplete Data via the EM Algorithm.

Kim, H.W.J.[Hyun-Woo J.], Bendlin, B.B.[Barbara B.], Adluru, N.[Nagesh], Collins, M.D.[Maxwell D.], Chung, M.K.[Moo K.], Johnson, S.C.[Sterling C.], Davidson, R.J.[Richard J.], Singh, V.[Vikas],
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images,
CVPR14(2705-2712)
IEEE DOI
Code, MGLM.
WWW Link. Multivariate general linear models

Lowe, D.G.[David G.],
Distinctive Image Features from Scale-Invariant Keypoints,
IJCV(60), No. 2, November 2004, pp. 91-110.
DOI Link
Code, SIFT.
Earlier:
Object Recognition from Local Scale-Invariant Features,
ICCV99(1150-1157).
IEEE DOI Award, ICCV Test of Time. New class of features, similar to neurons. Extract features that can be used in matching. SIFT -- Scale Invariant Feature Scale-space extrema detection; Feature localization; Orientation alignment; Feature descriptor. Code is available:
WWW Link. Note that this is the most cited paper according to one measure.
See also Scale Invariant Features, SIFT, SURF, ASIFT.

Russell, B.[Bryan], Torralba, A.B.[Antonio B.], Freeman, W.T.[William T.],
LableMe: The Open Annotation Tool,
Online2010.
WWW Link.
Dataset, Retrieval. Code, Annotation. The site for the annotation tool, also the video version.

Farhadi, M.[Mohammad], Yang, Y.Z.[Ye-Zhou],
TKD: Temporal Knowledge Distillation for Active Perception,
WACV20(942-951)
IEEE DOI
Code, Object Detection.
WWW Link. Temporal knowledge over NN applied over multiple frames. Adaptation models, Object detection, Visualization, Computational modeling, Task analysis, Training, Feature extraction

Yan, M., Zhao, M., Xu, Z., Zhang, Q., Wang, G., Su, Z.,
VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition,
LFR19(2647-2654)
IEEE DOI
Code, Face Recognition.
WWW Link. convolutional neural nets, face recognition, learning (artificial intelligence), student model, teacher model, knowledge distillation

Luo, W., Yang, X., Mo, X., Lu, Y., Davis, L., Li, J., Yang, J., Lim, S.,
Cross-X Learning for Fine-Grained Visual Categorization,
ICCV19(8241-8250)
IEEE DOI
Code, Learning.
WWW Link. feature extraction, image classification, image representation, neural nets, object recognition, supervised learning, Visualization

Haghighi, F.[Fatemeh], Taher, M.R.H.[Mohammad Reza Hosseinzadeh], Zhou, Z.W.[Zong-Wei], Gotway, M.B.[Michael B.], Liang, J.M.[Jian-Ming],
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning,
MedImg(40), No. 10, October 2021, pp. 2857-2868.
IEEE DOI
WWW Link. Code, Visual Worlds. Medical image annotation. Visualization, Semantics, Image representation, Feature extraction, Biomedical imaging, Annotations, Training, and 3D pre-trained models

Tanaka, M., Itamochi, T., Narioka, K., Sato, I., Ushiku, Y., Harada, T.,
Generating Easy-to-Understand Referring Expressions for Target Identifications,
ICCV19(5793-5802)
IEEE DOI
Code, Annotation.
WWW Link. computer games, image processing, referred objects, salient contexts, human annotation, Grand Theft Auto V, Task analysis

He, S., Tavakoli, H.R., Borji, A., Pugeault, N.,
Human Attention in Image Captioning: Dataset and Analysis,
ICCV19(8528-8537)
IEEE DOI
Code, Captioning.
WWW Link. convolutional neural nets, image segmentation, natural language processing, object detection, visual perception, Adaptation models

Huang, L., Wang, W., Chen, J., Wei, X.,
Attention on Attention for Image Captioning,
ICCV19(4633-4642)
IEEE DOI
Code, Captioning.
WWW Link. decoding, encoding, image processing, natural language processing, element-wise multiplication, image captioning, weighted average, Testing

Li, X.[Xin], Fan, D.P.[Deng-Ping], Yang, F.[Fan], Luo, A.[Ao], Cheng, H.[Hong], Liu, Z.C.[Zi-Cheng],
Probabilistic Model Distillation for Semantic Correspondence,
CVPR21(7501-7510)
IEEE DOI
WWW Link. Code, Matching. Codes, Annotations, Semantics, Training data, Probabilistic logic, Data models

Riu, C.[Clément], Nozick, V.[Vincent], Monasse, P.[Pascal],
Automatic RANSAC by Likelihood Maximization,
IPOL(12), 2022, pp. 27-49.
DOI Link
Code, RANSAC.
See also Likelihood-Ratio Test and Efficient Robust Estimation, The.

Birkfellner, W.[Wolfgang],
Applied Medical Image Processing: A Basic Course,
CRC PressBoca Raton, FL, September 17, 2010. ISBN: 9781439824443.
WWW Link. Buy this book: Applied Medical Image Processing: A Basic Course Code, Image Processing, Matlab. Code, Image Processing, Octave.

Ijaz, U.Z., Chaudhary, S.U., Don, M.S., Kim, K.Y.,
Computational strategies for protein quantitation in 2D electrophoresis gel image processor for Matlab,
FBIT07(129-134).
IEEE DOI Code, Image Analysis.

Stain Normalization toolbox for histopathology image analysis,
OnlineOctober 2014.
WWW Link. Code, Medical Analysis.
MATLAB implementation of well-known stain normalization algorithms.

Gary, K., Ibanez, L., Aylward, S., Gobbi, D., Blake, M.B., Cleary, K.,
IGSTK: an open source software toolkit for image-guided surgery,
Computer(39), No. 4, April 2006, pp. 46-53.
IEEE DOI
Code, Surgery.

Fodor, G.[Gábor], Cornelis, B.[Bruno], Yin, R.J.[Ru-Jie], Dooms, A.[Ann], Daubechies, I.[Ingrid],
Cradle Removal in X-Ray Images of Panel Paintings,
IPOL(7), 2017, pp. 23-42.
DOI Link
Code, X-Ray Analysis.

Mustra, M.[Mario], Grgic, M.[Mislav], Delac, K.,
Efficient presentation of DICOM mammography images using Matlab,
WSSIP08(13-16).
IEEE DOI
Code, Mammography.

Synergistic Image Reconstruction Framework SIRF,
2017. Code, PET Reconstruction. Code, MR Reconstruction.
WWW Link.
MATLAB and Python.

brain lesion segmentation,
Online
WWW Link.
Code, Brain Lesion Segmentation. multi-scale 3D Deep Convolutional Neural Network coupled with a 3D fully connected Conditional Random Field.

The Vascular Modeling Toolkit,
2016
WWW Link. Code, Vascular tree.

Kerautret, B.[Bertrand], Ngo, P.[Phuc], Passat, N.[Nicolas], Talbot, H.[Hugues], Jaquet, C.[Clara],
OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees,
IPOL(13), 2023, pp. 258-279.
DOI Link
Code, Vascular Tree.

APP for Monitoring Skin Spots,
2019.
WWW Link. Code, Skin Spots.
he app allows you to: take and organise photos of spots; compare two images of a spot side by side; email those images to someone (eg. doctor). That's all!

Xu, L., Jackowski, M., Goshtasby, A., Roseman, D., Bines, S., Yu, C., Dhawan, A., Huntley, A.,
Segmentation skin cancer images,
IVC(17), No. 1, January 1999, pp. 65-74.
Elsevier DOI Code, Segmentation. Software describe here is available from:
HTML Version.

Wu, X., Wen, N., Liang, J., Lai, Y., She, D., Cheng, M., Yang, J.,
Joint Acne Image Grading and Counting via Label Distribution Learning,
ICCV19(10641-10650)
IEEE DOI
Code, Dermatology.
WWW Link. diseases, learning (artificial intelligence), medical image processing, skin, joint acne image grading, Training

Xu, G.[Gang], Sugimoto, N.[Noriko],
Linear Algorithm for Motion from Three Weak Perspective Images Using Euler Angles,
PAMI(21), No. 1, January 1999, pp. 54-57.
IEEE DOI Code, Motion. Code is available:
HTML Version. Determine the rotations and structure. Uses epipolar geometry computations from:
See also Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach. But see results in:
See also Motion Estimation With More Than Two Frames.

Toyama, K.[Kentaro], Hager, G.D.[Gregory D.],
Incremental Focus of Attention for Robust Vision-Based Tracking,
IJCV(35), No. 1, November 1999, pp. 45-63.
DOI Link
Earlier:
Incremental Focus of Attention for Robust Visual Tracking,
CVPR96(189-195).
IEEE DOI Tracking.
HTML Version. And
PS File.
Earlier:
Tracker Fusion for Robustness in Visual Feature Tracking,
SPIE(2569), pp. 38-49. Photonics East, October 1995.
PS File. Code, Tracking. Code:
WWW Link.

Chen, B.X., Tsotsos, J.,
Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes,
VOT19(2281-2289)
IEEE DOI
Code, Tracking.
WWW Link. image segmentation, object tracking, ellipse fitting, real-time visual object tracking, Ellipse Fitting

Jiang, S.H.[Shi-Hao], Campbell, D.[Dylan], Lu, Y.[Yao], Li, H.D.[Hong-Dong], Hartley, R.I.[Richard I.],
Learning to Estimate Hidden Motions with Global Motion Aggregation,
ICCV21(9752-9761)
IEEE DOI
Code, Motion.
WWW Link. Image motion analysis, Codes, Ultraviolet sources, Estimation, Benchmark testing, Transformers, Motion and tracking, Stereo, 3D from multiview and other sensors

Yan, B., Zhao, H., Wang, D., Lu, H., Yang, X.,
'Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-Term Tracking,
ICCV19(2385-2393)
IEEE DOI
Code, Tracking.
WWW Link. object tracking, search problems, robust target verifier, tracked object, local search, image-wide global search.

Sinha, S.N.[Sudipta N.],
GPU_KLT: A GPU-based Implementation of the Kanade-Lucas-Tomasi Feature Tracker,
Online2007. Code, Tracking. C++ Code for Kanade-Lucas-Tomasi feature tracker.
See also Shape and Motion from Image Streams: A Factorization Method Part 3 - Detection and Tracking of Point Features.
See also KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker.

Bhat, G., Danelljan, M.[Martin], Van Gool, L.J., Timofte, R.,
Learning Discriminative Model Prediction for Tracking,
ICCV19(6181-6190)
IEEE DOI
Code, Tracking.
WWW Link. learning (artificial intelligence), object tracking, Siamese paradigm, Adaptation models

Zimmermann, K.[Karel], Matas, J.G.[Jirí G.], Svoboda, T.[Thomáš],
Tracking by an Optimal Sequence of Linear Predictors,
PAMI(31), No. 4, April 2009, pp. 677-692.
IEEE DOI
Code, Tracking. Dataset, Tracking.
Earlier: A1, A3, A2:
Simultaneous learning of motion and appearance,
MLMotion08(xx-yy).

Earlier: A1, A3, A2:
Adaptive Parameter Optimization for Real-time Tracking,
NRTL07(1-8).
IEEE DOI

Earlier: A1, A3, A2:
Multiview 3D Tracking with an Incrementally Constructed 3D Model,
3DPVT06(488-495).
IEEE DOI
Learning approach to tracking. Estimation of the pose given the pose of the previous frame. Matlab implementation available.
WWW Link.

Xu, T.Y.[Tian-Yang], Feng, Z.H.[Zhen-Hua], Wu, X.J.[Xiao-Jun], Kittler, J.V.[Josef V.],
Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters,
IJCV(129), No. 5, May 2021, pp. 1359-1375.
Springer DOI

Earlier:
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking,
ICCV19(7949-7959)
IEEE DOI
Code, Feature Selection.
WWW Link. Target tracking, Visualization, Correlation, Object tracking, Neural networks, Task analysis, Feature extraction, LASSO regression. correlation methods, image filtering, image representation, learning (artificial intelligence), Redundancy

Athar, A.[Ali], Mahadevan, S.[Sabarinath], Osep, A.[Aljosa], Leal-Taixé, L.[Laura], Leibe, B.[Bastian],
STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos,
ECCV20(XI:158-177).
Springer DOI
Code, Tracking.
WWW Link.

Zhang, W., Zhou, H., Sun, S., Wang, Z., Shi, J., Loy, C.C.,
Robust Multi-Modality Multi-Object Tracking,
ICCV19(2365-2374)
IEEE DOI
Code, Tracking.
WWW Link. feature extraction, image fusion, object detection, object tracking, target tracking, multiple sensors, Robustness

Adam, A.[Amit],
Fragments Tracker,
Online2008.
HTML Version. Code, Tracking.

Queirós, S., Morais, P., Barbosa, D., Fonseca, J.C., Vilaça, J.L., d'Hooge, J.,
MITT: Medical Image Tracking Toolbox,
MedImg(37), No. 11, November 2018, pp. 2547-2557.
IEEE DOI
Code, Tracking. Medical diagnostic imaging, Target tracking, Image sequences, Matlab, motion estimation

Wang, H.[Heng], Ullah, M.M.[Muhammad Muneeb], Klaser, A.[Alexander], Laptev, I.[Ivan], Schmid, C.[Cordelia],
Evaluation of local spatio-temporal features for action recognition,
BMVC09(xx-yy).
PDF File.
STIP features. Code, STIP.
WWW Link.

Yao, Y.[Yue], Zheng, L.[Liang], Yang, X.D.[Xiao-Dong], Napthade, M.[Milind], Gedeon, T.[Tom],
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond,
PAMI(46), No. 4, April 2024, pp. 2489-2505.
IEEE DOI

Earlier:
Simulating Content Consistent Vehicle Datasets with Attribute Descent,
ECCV20(VI:775-791).
Springer DOI
Code, Vehicle Synthesis.
WWW Link. Task analysis, Training, Synthetic data, Cameras, Engines, Training data, Attribute descent, data simulation, object-centric datasets

Bai, S.[Shuai], Zheng, Z.D.[Zhe-Dong], Wang, X.H.[Xiao-Han], Lin, J.Y.[Jun-Yang], Zhang, Z.[Zhu], Zhou, C.[Chang], Yang, H.X.[Hong-Xia], Yang, Y.[Yi],
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval,
AICity21(4029-4038)
IEEE DOI
Code, Vehicle Detection.
WWW Link. Training, Smart cities, Search problems, Robustness, Pattern recognition, Task analysis

Ranjan, V.[Viresh], Sharma, U.[Udbhav], Nguyen, T.[Thu], Hoai, M.[Minh],
Learning To Count Everything,
CVPR21(3393-3402)
IEEE DOI
WWW Link. Code, Counting. Visualization, Codes, Annotations, Animals, Detectors, Pattern recognition

Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J., Chellappa, R.,
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification,
ICCV19(6131-6140)
IEEE DOI
Code, Re-Identification.
WWW Link. feature extraction, learning (artificial intelligence), vehicle re-identification, attention models, Task analysis

Porrello, A.[Angelo], Bergamini, L.[Luca], Calderara, S.[Simone],
Robust Re-identification by Multiple Views Knowledge Distillation,
ECCV20(X:93-110).
Springer DOI
Code, Re-Identification.
WWW Link.

Liu, Y.[Yang], Wang, K.[Keze], Liu, L.B.[Ling-Bo], Lan, H.Y.[Hao-Yuan], Lin, L.[Liang],
TCGL: Temporal Contrastive Graph for Self-Supervised Video Representation Learning,
IP(31), 2022, pp. 1978-1993.
IEEE DOI
Code, Action Recognition.
WWW Link. Task analysis, Representation learning, Discrete cosine transforms, Legged locomotion, spatial-temporal data analysis

Xie, J., Pang, Y., Khan, M.H., Anwer, R.M., Khan, F.S., Shao, L.,
Mask-Guided Attention Network and Occlusion-Sensitive Hard Example Mining for Occluded Pedestrian Detection,
IP(30), 2021, pp. 3872-3884.
IEEE DOI

Earlier: A2, A1, A3, A4, A5, A6:
Mask-Guided Attention Network for Occluded Pedestrian Detection,
ICCV19(4966-4974)
IEEE DOI
Code, Pedestrian Detection.
WWW Link. Detectors, Standards, Feature extraction, Proposals, Benchmark testing, Task analysis, hard example mining. convolutional neural nets, image annotation, image classification, image segmentation, pedestrians, Computer architecture

Zhang, L., Zhu, X., Chen, X., Yang, X., Lei, Z., Liu, Z.,
Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection,
ICCV19(5126-5136)
IEEE DOI
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, feature extraction, image colour analysis, image fusion, infrared imaging, Color

Yan, Z.Y.[Zhao-Yi], Yuan, Y.C.[Yu-Chen], Zuo, W.M.[Wang-Meng], Tan, X.[Xiao], Wang, Y.Z.[Ye-Zhen], Wen, S.L.[Shi-Lei], Ding, E.R.[Er-Rui],
Perspective-Guided Convolution Networks for Crowd Counting,
ICCV19(952-961)
IEEE DOI
Code, Convolutional Networks.
WWW Link. convolutional neural nets, image resolution, object detection, perspective-guided convolution networks, Benchmark testing

Siebel, N.T.[Nils T],
Design and Implementation of People Tracking Algorithms for Visual Surveillance Applications,
Ph.D.Thesis, March 2003, Department of Computer Science, The University of Reading, Reading, UK.
PDF File. Code, Tracking.
WWW Link.

Zhou, K.Y.[Kai-Yang], Yang, Y.X.[Yong-Xin], Cavallaro, A.[Andrea], Xiang, T.[Tao],
Learning Generalisable Omni-Scale Representations for Person Re-Identification,
PAMI(44), No. 9, September 2022, pp. 5056-5069.
IEEE DOI

Earlier:
Omni-Scale Feature Learning for Person Re-Identification,
ICCV19(3701-3711)
IEEE DOI
Code, Re-Identification.
WWW Link. Adaptation models, Convolutional codes, Architecture, Cameras, Feature extraction, Data models, Convolution, neural architecture search. convolutional neural nets, graph theory, image representation, learning (artificial intelligence), Convolution

Fu, Y., Wei, Y., Wang, G., Zhou, Y., Shi, H., Uiuc, U., Huang, T.,
Self-Similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-Identification,
ICCV19(6111-6120)
IEEE DOI
Code, Re-Identification.
WWW Link. pattern clustering, unsupervised learning, DukeMTMC?Market1501, clustering-guided semisupervised approach, Machine learning

Chen, B., Deng, W., Hu, J.,
Mixed High-Order Attention Network for Person Re-Identification,
ICCV19(371-381)
IEEE DOI
Code, Re-Identification.
WWW Link. image processing, learning (artificial intelligence), statistics, mixed high-order attention network, person re-identification, Cameras

Yu, T., Li, D., Yang, Y., Hospedales, T., Xiang, T.,
Robust Person Re-Identification by Modelling Feature Uncertainty,
ICCV19(552-561)
IEEE DOI
Code, Re-Identification.
WWW Link. feature extraction, Gaussian distribution, learning (artificial intelligence), neural nets, Cameras

Sun, H., Chen, Z., Yan, S., Xu, L.,
MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification,
ICCV19(6736-6746)
IEEE DOI
Code, Re-Identification.
WWW Link. graph theory, image matching, learning (artificial intelligence), convergence rate, maximum-value perfect matching, Benchmark testing

Guo, J., Yuan, Y., Huang, L., Zhang, C., Yao, J., Han, K.,
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification,
ICCV19(3641-3650)
IEEE DOI
Code, Re-Identification.
WWW Link. image representation, dual part-aligned representations, person re-identification, externally defined attributes, Visualization

Luo, C.C.[Chuan-Chen], Chen, Y.T.[Yun-Tao], Wang, N.Y.[Nai-Yan], Zhang, Z.X.[Zhao-Xiang],
Spectral Feature Transformation for Person Re-Identification,
ICCV19(4975-4984)
IEEE DOI
Code, Re-Identification.
WWW Link. graph theory, image processing, learning (artificial intelligence), pattern clustering, Benchmark testing

Baseline Algorithm and Performance for Gait Based Human ID Challenge Problem,
2004, USF.
WWW Link. Dataset, Gait. Code, Gait.

CMU Graphics Lab Motion Capture Database,
2004.
WWW Link. Dataset, Motion Capture. Code, Motion Capture. 2000+ examples of motion capture data. Includes some software.

Mao, W., Liu, M., Salzmann, M., Li, H.,
Learning Trajectory Dependencies for Human Motion Prediction,
ICCV19(9488-9496)
IEEE DOI
Code, Human Motion.
WWW Link. graph theory, image motion analysis, image representation, learning (artificial intelligence), pose estimation, Hidden Markov models

Bargiotas, I.[Ioannis], Audiffren, J.[Julien], Vayatis, N.[Nicolas], Vidal, P.P.[Pierre-Paul], Yelnik, A.P.[Alain P.], Ricard, D.[Damien],
Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm,
IPOL(9), 2019, pp. 143-157.
DOI Link
Code, Posture. postural control evaluation in elderly.

Parmar, P.[Paritosh], Morris, B.[Brendan],
Win-Fail Action Recognition,
Activity22(161-171)
IEEE DOI
Code, Action Recognition.
WWW Link. Did the attempt fail or succeed. Analytical models, Pediatrics, Limiting, Benchmark testing, Spatial databases, Spatiotemporal phenomena, Pattern recognition

Liu, Y.Z.[Yuan-Zhong], Yuan, J.S.[Jun-Song], Tu, Z.G.[Zhi-Gang],
Motion-Driven Visual Tempo Learning for Video-Based Action Recognition,
IP(31), 2022, pp. 4104-4116.
IEEE DOI
WWW Link. Code, Action Recognition. Feature extraction, Visualization, Correlation, Dynamics, Semantics, Spatiotemporal phenomena, Action recognition, visual tempo, temporal correlation module

Neimark, D.[Daniel], Bar, O.[Omri], Zohar, M.[Maya], Asselmann, D.[Dotan],
Video Transformer Network,
CVEU21(3156-3165)
IEEE DOI
WWW Link. Code, Video Recognition. Solid modeling, Runtime, Computational modeling, Video sequences, Machine learning, Benchmark testing

Punnakkal, A.R.[Abhinanda R.], Chandrasekaran, A.[Arjun], Athanasiou, N.[Nikos], Quirós-Ramírez, A.[Alejandra], Black, M.J.[Michael J.],
BABEL: Bodies, Action and Behavior with English Labels,
CVPR21(722-731)
IEEE DOI
WWW Link. Code, Human Action. Location awareness, Solid modeling, Motion segmentation, Semantics, Benchmark testing

Aksan, E., Kaufmann, M., Hilliges, O.,
Structured Prediction Helps 3D Human Motion Modelling,
ICCV19(7143-7152)
IEEE DOI
Code, Human Motion.
WWW Link. feature extraction, graph theory, image motion analysis, learning (artificial intelligence), Kinematics

Zhang, J., Felsen, P., Kanazawa, A., Malik, J.,
Predicting 3D Human Dynamics From Video,
ICCV19(7113-7122)
IEEE DOI
Code, Human Motion.
WWW Link. autoregressive processes, image motion analysis, image sequences, solid modelling, video signal processing, Shape

Narayan, S., Cholakkal, H., Khan, F.S., Shao, L.,
3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization,
ICCV19(8678-8686)
IEEE DOI
Code, Counting.
WWW Link. feature extraction, image classification, image sequences, video signal processing, Motion pictures

Wehrmann, J., Lopes, M.A., Souza, D., Barros, R.,
Language-Agnostic Visual-Semantic Embeddings,
ICCV19(5803-5812)
IEEE DOI
Code, Visualization.
WWW Link. data visualisation, information retrieval, learning (artificial intelligence), Architecture

Zhu, X.Q.[Xin-Qi], Xu, C.[Chang], Hui, L.W.[Lang-Wen], Lu, C.W.[Ce-Wu], Tao, D.C.[Da-Cheng],
Approximated Bilinear Modules for Temporal Modeling,
ICCV19(3493-3502)
IEEE DOI
Code, Convolutional Neural Networks.
WWW Link. Fine-grained models. convolutional neural nets, feature extraction, image classification, image representation, inference mechanisms, Image recognition

Furnari, A., Farinella, G.M.[Giovanni Maria],
What Would You Expect? Anticipating Egocentric Actions With Rolling-Unrolling LSTMs and Modality Attention,
ICCV19(6251-6260)
IEEE DOI
Code, Egocentric Actions.
WWW Link. cameras, feature extraction, image classification, image colour analysis, image motion analysis, Computational modeling

Feichtenhofer, C.[Christoph], Fan, H., Malik, J., He, K.,
SlowFast Networks for Video Recognition,
ICCV19(6201-6210)
IEEE DOI
Code, Video Processing.
WWW Link. image capture, image classification, image motion analysis, learning (artificial intelligence), video signal processing, Channel capacity

Ye, Y., Singh, M., Gupta, A.[Abhinav], Tulsiani, S.[Shubham],
Compositional Video Prediction,
ICCV19(10352-10361)
IEEE DOI
Code, Motion.
WWW Link. image motion analysis, stochastic processes, video signal processing, realistic stochastic video prediction, Encoding

Tian, Y.[Yuan], Yan, Y.C.[Yi-Chao], Zhai, G.T.[Guang-Tao], Guo, G.D.[Guo-Dong], Gao, Z.Y.[Zhi-Yong],
EAN: Event Adaptive Network for Enhanced Action Recognition,
IJCV(130), No. 10, October 2022, pp. 2453-2471.
Springer DOI
Code, Action Recognition.
WWW Link.

Tavakolian, M., Tavakoli, H.R., Hadid, A.,
AWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation,
ICCV19(8019-8028)
IEEE DOI
Code, Video Analysis.
WWW Link. Gaussian processes, image classification, image representation, image segmentation, spatiotemporal phenomena, Covariance matrices

Zheng, A.[Anlin], Zhang, Y.[Yuang], Zhang, X.Y.[Xiang-Yu], Qi, X.J.[Xiao-Juan], Sun, J.[Jian],
Progressive End-to-End Object Detection in Crowded Scenes,
CVPR22(847-856)
IEEE DOI
Code, Object Detection.
WWW Link. Representation learning, Performance evaluation, Deep learning, Machine vision, Detectors, Prediction methods, Object detection, Vision applications and systems

Shi, Z.S.[Zhen-Sheng], Guan, C.[Cheng], Cao, L.J.[Liang-Jie], Li, Q.Q.[Qian-Qian], Liang, J.[Ju], Gu, Z.R.[Zhao-Rui], Zheng, H.Y.[Hai-Yong], Zheng, B.[Bing],
CoTeRe-net: Discovering Collaborative Ternary Relations in Videos,
ECCV20(VI:379-396).
Springer DOI
Code, Action Recognition.
WWW Link. Relations for action and behavior recognition.

Rebecq, H.[Henri], Ranftl, R.[René], Koltun, V.[Vladlen], Scaramuzza, D.[Davide],
High Speed and High Dynamic Range Video with an Event Camera,
PAMI(43), No. 6, June 2021, pp. 1964-1980.
IEEE DOI

Earlier:
Events-To-Video: Bringing Modern Computer Vision to Event Cameras,
CVPR19(3852-3861).
IEEE DOI
Code, HDR. Dataset, HDR. Dataset, E2VID.
HTML Version. Image reconstruction, Cameras, Streaming media, Dynamic range, Brightness, Heuristic algorithms, high dynamic range

Gehrig, D., Gehrig, M., Hidalgo-Carrió, J., Scaramuzza, D.,
Video to Events: Recycling Video Datasets for Event Cameras,
CVPR20(3583-3592)
IEEE DOI
PDF File.
Code, Event Camera.
WWW Link. ESIM: Event camera simulator:
WWW Link. Video:
WWW Link. Cameras, Sensors, Semantics, Standards, Brightness, Task analysis, Machine learning

Freifeld, O.[Oren], Hauberg, S.[Soren], Batmanghelich, K.[Kayhan], Fisher, J.W.[John W.],
Transformations Based on Continuous Piecewise-Affine Velocity Fields,
PAMI(39), No. 12, December 2017, pp. 2496-2509.
IEEE DOI

Earlier:
Highly-Expressive Spaces of Well-Behaved Transformations: Keeping it Simple,
ICCV15(2911-2919)
IEEE DOI
Code, Tranformations.
WWW Link. Biomedical imaging, Complexity theory, Computational modeling, Distribution functions, Histograms, Trajectory, Spatial transformations, continuous piecewise-affine velocity fields, diffeomorphisms, tessellations, priors, MCMC

Wang, Z.Q.[Zi-Qin], Xu, J.[Jun], Liu, L.[Li], Zhu, F.[Fan], Shao, L.[Ling],
RANet: Ranking Attention Network for Fast Video Object Segmentation,
ICCV19(3977-3986)
IEEE DOI
Code, Video Object Segmentatioin.
WWW Link. image matching, image segmentation, neural nets, object detection, supervised learning, video signal processing, Real-time systems

Rao, S.R.[Shankar R.], Tron, R.[Roberto], Vidal, R.[Rene], Ma, Y.[Yi],
Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories,
PAMI(32), No. 10, October 2010, pp. 1832-1845.
IEEE DOI
Code, Motion Segmentation.
Earlier:
Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories,
CVPR08(1-8).
IEEE DOI
Segmenting tracked trajectories, occlusions, deformations lead to problems. Develop robust separation scheme to deal with these issues. Related to lossy compression. Code is available.
WWW Link.

Duarte, K., Rawat, Y., Shah, M.,
CapsuleVOS: Semi-Supervised Video Object Segmentation Using Capsule Routing,
ICCV19(8479-8488)
IEEE DOI
Code, Video Segmentation.
WWW Link. image motion analysis, image segmentation, image sequences, object tracking, video signal processing, CapsuleVOS, Computer architecture

Panorama Tools,
2006.
WWW Link. Code, Image Stitching. Mosaic Generation software.

XuvTools: eXtend yoUr View Toolkit,
2008
HTML Version. Code, Image Stitching. Mosaic generation software and High Dynamic Range software.

Buades, A.[Antoni], Coll, B.[Bartomeu], Morel, J.M.[Jean-Michel], Sbert, C.[Catalina],
Self-Similarity Driven Color Demosaicking,
IP(18), No. 6, June 2009, pp. 1192-1202.
IEEE DOI

And:
Self-Similarity Driven Demosaicking,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Demosaicking.

Getreuer, P.[Pascal],
Malvar-He-Cutler Linear Image Demosaicking,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Demosaicking.
See also High-quality linear interpolation for demosaicing of Bayer-patterned color images.

Getreuer, P.[Pascal],
Zhang-Wu Directional LMMSE Image Demosaicking,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Demosaicking.
See also Color Demosaicking Via Directional Linear Minimum Mean Square-Error Estimation.
See also Image Demosaicking with Contour Stencils.

Getreuer, P.[Pascal],
Gunturk-Altunbasak-Mersereau Alternating Projections Image Demosaicking,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Demosaicking.
See also Color plane interpolation using alternating projections.

Getreuer, P.[Pascal],
Image Demosaicking with Contour Stencils,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Demosaicking.
See also Zhang-Wu Directional LMMSE Image Demosaicking.
See also Image Interpolation with Contour Stencils.
See also Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman.

Wang, Y.Q.[Yi-Qing], Limare, N.[Nicolas],
A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing,
IPOL(5), 2015, pp. 257-266.
DOI Link
Code, Demosaicking.
See also multilayer neural network for image demosaicking, A.

Mei, K., Li, J., Zhang, J., Wu, H., Li, J., Huang, R.,
HighEr-Resolution Network for Image Demosaicing and Enhancing,
AIM19(3441-3448)
IEEE DOI
Code, Demosaicking.
WWW Link. feature extraction, image colour analysis, image enhancement, image resolution, image restoration, image segmentation, Deep Neural Networks

Nomura, Y.[Yoshikuni], Zhang, L., Nayar, S.K.[Shree K.],
Scene Collages and Flexible Camera Arrays,
ConferenceEurographics Symposium on Rendering, Jun, 2007.
PDF File.
WWW Link. Code, Mosaic.

Super-Resolution Code,
Online2007. Code, Super-Resolution.
HTML Version. Matlab/C code.
See also Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?.

Milanfar, P.[Peyman], (Ed.)
Super-Resolution Imaging,
CRC PressBoca Raton, FL, September 28, 2010. ISBN: 9781439819302
WWW Link. Buy this book: Super-Resolution Imaging (Digital Imaging and Computer Vision) Code, Super-Resolution. Survey, Super-Resolution.

Lafenetre, J.[Jamy], Facciolo, G.[Gabriele], Eboli, T.[Thomas],
Implementing Handheld Burst Super-Resolution,
IPOL(13), 2023, pp. 227-257.
DOI Link
WWW Link. Code, Super Resolution.
See also Handheld Multi-Frame Super-Resolution.

Wang, J.Q.[Jia-Qi], Chen, K.[Kai], Xu, R.[Rui], Liu, Z.W.[Zi-Wei], Loy, C.C.[Chen Change], Lin, D.[Dahua],
CARAFE: Content-Aware ReAssembly of FEatures,
ICCV19(3007-3016)
IEEE DOI
Code, Convolutional Networks.
WWW Link. Feature upsampling. convolutional neural nets, image segmentation, interpolation, learning (artificial intelligence), object detection, CARAFE, Image segmentation

Li, J.C.[Jun-Cheng], Yuan, Y.T.[Yi-Ting], Mei, K.F.[Kang-Fu], Fang, F.M.[Fa-Ming],
Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution,
CLI19(3814-3823)
IEEE DOI
Code, Super Resolution.
WWW Link. convolutional neural nets, fractals, image resolution, learning (artificial intelligence), topology, CNN

Hui, L.[Le], Xu, R.[Rui], Xie, J.[Jin], Qian, J.J.[Jian-Jun], Yang, J.[Jian],
Progressive Point Cloud Deconvolution Generation Network,
ECCV20(XV:397-413).
Springer DOI
Code, Point Cloud.
WWW Link. Interpolation.

Huang, J.B.[Jia-Bin], Singh, A.[Abhishek], Ahuja, N.[Narendra],
Single image super-resolution from transformed self-exemplars,
CVPR15(5197-5206)
IEEE DOI
Code, Super Resolution.
WWW Link.
See also Set5, Set14, Urban 100, BSD 100, Sun-Hays 80 Datasets.

Getreuer, P.[Pascal],
Image Interpolation with Contour Stencils,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Image Interpolation.
Earlier:
Contour stencils for edge-adaptive image interpolation,
SPIE(7257), 2009.
DOI Link
Earlier:
Image zooming with contour stencils,
SPIE(7246), 2009.
DOI Link
See also Image Demosaicking with Contour Stencils.

Getreuer, P.[Pascal],
Image Interpolation with Geometric Contour Stencils,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Image Interpolation.

Getreuer, P.[Pascal],
Roussos-Maragos Tensor-Driven Diffusion for Image Interpolation,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Image Interpolation.
See also Reversible Interpolation of Vectorial Images by an Anisotropic Diffusion-Projection PDE.

Getreuer, P.[Pascal],
Linear Methods for Image Interpolation,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Image Interpolation.

Briand, T.[Thibaud], Monasse, P.[Pascal],
Theory and Practice of Image B-Spline Interpolation,
IPOL(8), 2018, pp. 99-141.
DOI Link
Code, Splines.

Briand, T.[Thibaud],
Reversibility Error of Image Interpolation Methods: Definition and Improvements,
IPOL(9), 2019, pp. 360-380.
DOI Link
Code, Interpolation.

Li, Y.[Yanhao], Gardella, M.[Marina], Bammey, Q.[Quentin], Nikoukhah, T.[Tina], Grompone-von Gioi, R.[Rafael], Colom, M.[Miguel], Morel, J.M.[Jean-Michel],
A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression,
IPOL(13), 2023, pp. 280-313.
DOI Link
Code, Video Noise.
Earlier:
Video Signal-Dependent Noise Estimation via Inter-Frame Prediction,
ICIP22(1406-1410)
IEEE DOI
Gaussian noise, Redundancy, Estimation, Energy measurement, Transforms, High frequency, Noise estimation, image processing, noise level function

Tulyakov, S.[Stepan], Gehrig, D.[Daniel], Georgoulis, S.[Stamatios], Erbach, J.[Julius], Gehrig, M.[Mathias], Li, Y.[Yuanyou], Scaramuzza, D.[Davide],
Time Lens: Event-based Video Frame Interpolation,
CVPR21(16150-16159)
IEEE DOI
HTML Version.
Code, Frame Interpolation. Dataset, Frame Interpolation. Interpolation, Visualization, Image color analysis, Benchmark testing, Cameras, Sensors, Pattern recognition

Yu, S., Park, B., Jeong, J.,
PoSNet: 4x Video Frame Interpolation Using Position-Specific Flow,
AIM19(3503-3511)
IEEE DOI
Code, Video Interpolation.
WWW Link. image resolution, image sequences, interpolation, video signal processing, video frame interpolation, Video Enhancement

Buades, A.[Antoni], Haro, G.[Gloria], Meinhardt-Llopis, E.[Enric],
Obtaining High Quality Photographs of Paintings by Image Fusion,
IPOL(5), 2015, pp. 159-175.
DOI Link
Code, HDR.
See also Photographing Paintings by Image Fusion.

Buades, A.[Antoni], Lisani, J.L.[Jose-Luis],
Video Denoising with Optical Flow Estimation,
IPOL(8), 2018, pp. 142-166.
DOI Link
Code, Video Denoising.
Earlier:
Dual domain video denoising with optical flow estimation,
ICIP17(2986-2990)
IEEE DOI
Image color analysis, Noise measurement, Noise reduction, Principal component analysis, video denoising

Hessel, C.[Charles],
An Implementation of the Exposure Fusion Algorithm,
IPOL(8), 2018, pp. 369-387.
DOI Link
Code, High Dynamic Range.

Monod, A.[Antoine], Delon, J.[Julie], Veit, T.[Thomas],
An Analysis and Implementation of the HDR+ Burst Denoising Method,
IPOL(11), 2021, pp. 142-169.
DOI Link
Code, High Dynamic Range. Code, Python.
See also Burst photography for high dynamic range and low-light imaging on mobile cameras.

Manders, C.[Corey], Farbiz, F.[Farzam], Mann, S.[Steve],
A Compression Method for Arbitrary Precision Floating-Point Images,
ICIP07(IV: 165-168).
IEEE DOI
Code, Image Compression.
HTML Version. Reorganize the data then use JPEG or other compression. For high dynamic range images.

Dong, C.Q.[Chao-Qun], Sroubek, F.[Filip], Portilla, J.[Javier],
Spectral Pre-Adaptation for Restoring Real-World Blurred Images using Standard Deconvolution Methods,
IPOL(12), 2022, pp. 218--346.
DOI Link
Code, Deblurring.

Song, C.[Chen], Huang, Q.X.[Qi-Xing], Bajaj, C.[Chandrajit],
E-CIR: Event-Enhanced Continuous Intensity Recovery,
CVPR22(7793-7802)
IEEE DOI
Code, Motion Blur.
WWW Link. Deep learning, Visualization, Presses, Predictive models, Cameras, Probabilistic logic, Image and video synthesis and generation

Anger, J.[Jérémy], Facciolo, G.[Gabriele], Delbracio, M.[Mauricio],
Estimating an Image's Blur Kernel Using Natural Image Statistics, and Deblurring it: An Analysis of the Goldstein-Fattal Method,
IPOL(8), 2018, pp. 282-304.
DOI Link
Code, Blur.
See also Blur-Kernel Estimation from Spectral Irregularities.

Eboli, T.[Thomas], Morel, J.M.[Jean-Michel], Facciolo, G.[Gabriele],
Breaking down Polyblur: Fast Blind Correction of Small Anisotropic Blurs,
IPOL(12), 2022, pp. 435-456.
DOI Link
Code, Deblurring. Polyblur
See also Polyblur: Removing mild blur by polynomial reblurring.

Getreuer, P.[Pascal],
Total Variation Deconvolution Using Split Bregman,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Total Variation. Deblurring
See also Nonlinear total variation based noise removal algorithms. Goldstein and Osher

Anger, J.[Jérémy], Meinhardt-Llopis, E.[Enric],
Implementation of Local Fourier Burst Accumulation for Video Deblurring,
IPOL(7), 2017, pp. 56-64.
DOI Link
Code, Video Deblurring.
See also Removing Camera Shake via Weighted Fourier Burst Accumulation.

Feschet, F.[Fabien],
Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors,
IPOL(9), 2019, pp. 158-182.
DOI Link
Code, Denoising. patch-based denoising algorithm.
See also Image Denoising Using the Higher Order Singular Value Decomposition.

Park, B., Yu, S., Jeong, J.,
Robust Temporal Super-Resolution for Dynamic Motion Videos,
AIM19(3494-3502)
IEEE DOI
Code, Super Resolution.
WWW Link. image motion analysis, image resolution, image sequences, learning (artificial intelligence), neural nets, Deep learning

Birchfield, S.T.[Stan T.],
KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker,
Online1997. Code, Tracking.
WWW Link.
See also GPU_KLT: A GPU-based Implementation of the Kanade-Lucas-Tomasi Feature Tracker.

Zhou, H.Z.[Hui-Zhong], Ummenhofer, B.[Benjamin], Brox, T.[Thomas],
DeepTAM: Deep Tracking and Mapping with Convolutional Neural Networks,
IJCV(128), No. 3, March 2020, pp. 756-769.
Springer DOI
Code, Tracking.
WWW Link.

Zhang, H.K.[Hao-Kui], Li, Y.[Ying], Cao, Y.Z.H.[Yuan-Zhou-Han], Liu, Y.[Yu], Shen, C.H.[Chun-Hua], Yan, Y.L.[You-Liang],
Exploiting Temporal Consistency for Real-Time Video Depth Estimation,
ICCV19(1725-1734)
IEEE DOI
Code, Depth from Motion.
WWW Link. convolutional neural nets, learning (artificial intelligence), video signal processing, Streaming media

Jacobs, D.W.[David W.],
Linear Fitting with Missing Data for Structure-from-Motion,
CVIU(82), No. 1, April 2001, pp. 57-81.
DOI Link
Code, Surface Fitting. Code:
WWW Link.
Earlier:
Linear Fitting with Missing Data: Applications to Structure from Motion and to Characterizing Intensity Images,
CVPR97(206-212).
IEEE DOI
Problems reduce to fitting surface to data.

Garrido, L.[Lluís], Kalmoun, E.[El_Mostafa],
A Line Search Multilevel Truncated Newton Algorithm for Computing the Optical Flow,
IPOL(5), 2015, pp. 124-138.
DOI Link
Code, Optical Flow.

Monzón, N.[Nelson], Salgado, A.[Agustín], Sánchez, J.[Javier],
Robust Discontinuity Preserving Optical Flow Methods,
IPOL(6), 2016, pp. 165-182.
DOI Link
Code, Optical Flow.

Sánchez, J.[Javier],
The Inverse Compositional Algorithm for Parametric Registration,
IPOL(6), 2016, pp. 212-232.
DOI Link
Code, Optical Flow.

Garamendi, J.F.[Juan Francisco], Lazcano, V.[Vanel], Ballester, C.[Coloma],
Joint TV-L1 Optical Flow and Occlusion Estimation,
IPOL(9), 2019, pp. 432-452.
DOI Link
Code, Optical Flow.

Raad, L.[Lara], Oliver, M.[Maria], Ballester, C.[Coloma], Haro, G.[Gloria], Meinhardt, E.[Enric],
On Anisotropic Optical Flow Inpainting Algorithms,
IPOL(10), 2020, pp. 78-104.
DOI Link
Code, Optical Flow.
See also Motion Inpainting by an Image-Based Geodesic AMLE Method.

Zach, C.[Christopher],
Gain-Adaptive KLT Tracking and TV-L1 optical flow on the GPU,
2010 Code, Optic Flow.
HTML Version.

Barron, J.L., Fleet, D.J., and Beauchemin, S.S.,
Performance of Optical Flow Techniques,
IJCV(12), No. 1, February 1994, pp. 43-77.
Springer DOI
HTML Version.
WWW Link.
And: Add: Burkitt, T.A., CVPR92(236-242).
IEEE DOI Code, Optic Flow. Survey, Optic Flow. Survey of the field and a comparison of a variety of techniques. Compares quality of results, not execution time. Compares: Lucas/Kanade (
See also Generalized Image Matching by the Method of Differences. ), Fleet/Jepson (
See also Hierarchial Construction of Orientation and Velocity Selective Filters. ), Uras (
See also Computational Approach to Motion Perception, A. ), Nagel (
See also On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences. ), Anandan (
See also Computational Framework and an Algorithm for the Measurement of Visual Motion, A. ), Horn/Shunck (
See also Determining Optical Flow. ), Singh (
See also Image-Flow Computation: An Estimation-Theoretic Framework and a Unified Perspective. ). Code for all of these is available from:
WWW Link.

McCane, B.[Brendan],
Optic Flow Evaluation,
OnlineMarch 2007.
WWW Link. Code, Optic Flow.

Dagobert, T.[Tristan], Monzón, N.[Nelson], Sánchez, J.[Javier],
Comparison of Optical Flow Methods under Stereomatching with Short Baselines,
IPOL(9), 2019, pp. 329-359.
DOI Link
Code, Optical Flow. Code for: Lucas-Kanade 1D, Robust Optical Flow 1D and oRobust Discontinuity Preserving 1D.

Gehrig, M.[Mathias], Millhäusler, M.[Mario], Gehrig, D.[Daniel], Scaramuzza, D.[Davide],
E-RAFT: Dense Optical Flow from Event Cameras,
3DV21(197-206)
IEEE DOI
HTML Version.
Code, Optical Flow. Paper, Video, Code: Training, Image motion analysis, Correlation, Costs, Estimation, Computer architecture, optical flow, event cameras

He, P.[Pan], Emami, P.[Patrick], Ranka, S.[Sanjay], Rangarajan, A.[Anand],
Learning Scene Dynamics from Point Cloud Sequences,
IJCV(130), No. 3, March 2022, pp. 669-695.
Springer DOI
WWW Link. Code, Scene Flow.

Black, M.J., Anandan, P.,
The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow-Fields,
CVIU(63), No. 1, January 1996, pp. 75-104.
DOI Link
HTML Version. Code:
HTML Version. Code, Optic Flow.
Earlier:
A Framework for the Robust Estimation of Optical Flow,
ICCV93(231-236).
IEEE DOI Award, ICCV Test of Time.
Earlier:
Robust Dynamic Motion Estimation Over Time,
CVPR91(296-302).
IEEE DOI
HTML Version. Award, CVPR.
And:
The Robust Estimation of Multiple Motions: Affine and Piecewise-Smooth Flow-Fields,
TR Xerox PARC, December 1993. Motion, Many Frames. Incremental tracking system using multiple resolutions.

SVO Pro: Semi-direct Visual-Inertial Odometry and SLAM for Monocular, Stereo, and Wide Angle Cameras,

HTML Version.
WWW Link.
Code, Visual Odometry. Includes: Visual-odometry: The most recent version of SVO that supports perspective and fisheye/catadioptric cameras in monocular or stereo setup. It also includes active exposure control. Visual-inertial odometry: SVO fronted + visual-inertial sliding window optimization backend (modified from OKVIS) Visual-inertial SLAM: SVO frontend + visual-inertial sliding window optimization backend + globally bundle adjusted map (using iSAM2). The global map is updated in real-time, thanks to iSAM2, and used for localization at frame-rate. Visual-inertial SLAM with loop closure: Loop closures, via DBoW2, are integrated in the global bundle adjustment. Pose graph optimization is also included as a lightweight replacement of the global bundle adjustment.

Polizzi, V.[Vincenzo], Hewitt, R.[Robert], Hidalgo-Carrió, J.[Javier], Delaune, J.[Jeff], Scaramuzza, D.[Davide],
Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry,
RALetters(7), No. 4, 2022, pp. 10681-10688.
IEEE DOI Code, Odometry.
HTML Version.

Hidalgo-Carrió, J.[Javier], Gallego, G.[Guillermo], Scaramuzza, D.[Davide],
Event-aided Direct Sparse Odometry,
CVPR22(5771-5780)
IEEE DOI
Code, Odometry.
WWW Link. Bundle adjustment, Tracking loops, Technological innovation, Sensitivity, Codes, Target tracking, Brightness, Low-level vision, Robot vision

Yang, G., Huang, X., Hao, Z., Liu, M., Belongie, S., Hariharan, B.,
PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows,
ICCV19(4540-4549)
IEEE DOI
Code, 3D.
WWW Link. Bayes methods, feature extraction, image reconstruction, image representation, learning (artificial intelligence), Solid modeling

Mandroux, N.[Nicolas], Dagobert, T.[Tristan], Drouyer, S.[Sébastien], Grompone von Gioi, R.[Rafael],
Single Date Wind Turbine Detection on Sentinel-2 Optical Images,
IPOL(12), 2022, pp. 198-217.
DOI Link
Code, Wind Turbine.

Camus, T.A.,
Real-Time Quantized Optical Flow,
RealTimeImg(3), 1997, pp. 71-86.
Earlier: CAMP95(xx). Implementation of algorithm. Code, Optic Flow.
WWW Link.

Roques, A.[Axel], Zhao, A.[Anne],
Association Rules Discovery of Deviant Events in Multivariate Time Series: An Analysis and Implementation of the SAX-ARM Algorithm,
IPOL(12), 2022, pp. 604-624.
DOI Link
Code, Time Series. Tims Series analysis.

Zhou, H., Liu, Z., Xu, X., Luo, P., Wang, X.,
Vision-Infused Deep Audio Inpainting,
ICCV19(283-292)
IEEE DOI
Code, Inpainting.
WWW Link. audio signal processing, audio-visual systems, image restoration, image segmentation, multimodality perception,

Mo, D.M.[Dong-Mei], Lai, Z.H.[Zhi-Hui],
Robust Jointly Sparse Regression with Generalized Orthogonal Learning for Image Feature Selection,
PR(93), 2019, pp. 164-178.
Elsevier DOI
Code, Matlab.
WWW Link. Dimensionality reduction, Local structure, Joint sparsity, Orthogonality, Orthogonal matching pursuit

Painsky, A., Rosset, S.,
Isotonic Modeling with Non-Differentiable Loss Functions with Application to Lasso Regularization,
PAMI(38), No. 2, February 2016, pp. 308-321.
IEEE DOI
Algorithm design and analysis Code, Regularization. Implementation:
WWW Link.

Affeldt, S.[Séverine], Labiod, L.[Lazhar], Nadif, M.[Mohamed],
CAEclust: A Consensus of Autoencoders Representations for Clustering,
IPOL(12), 2022, pp. 590-603.
DOI Link
Code, Spectral Clustering.

Philion, J.[Jonah], Kar, A.[Amlan], Fidler, S.[Sanja],
Learning to Evaluate Perception Models Using Planner-Centric Metrics,
CVPR20(14052-14061)
IEEE DOI
Code, Evaluation.
WWW Link. Detectors, Task analysis, Object detection, Noise measurement, Trajectory

Rakin, A.S., He, Z., Fan, D.,
TBT: Targeted Neural Network Attack With Bit Trojan,
CVPR20(13195-13204)
IEEE DOI

Earlier:
Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search,
ICCV19(1211-1220)
IEEE DOI
Code, Neural Networks.
WWW Link. Trojan horses, Training, Computational modeling, Neurons, Training data, Quantization (signal), Neural networks. gradient methods, neural nets, security of data, Bit-flip attack, Deep Neural Network, DNN weight attack methodology, Degradation

Chen, M., Kira, Z., Alregib, G., Yoo, J., Chen, R., Zheng, J.,
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation,
ICCV19(6320-6329)
IEEE DOI
Code, Domain Adaption.
WWW Link. convolutional neural nets, image classification, learning (artificial intelligence), neural net architecture, Dynamics

Chen, M., Xue, H., Cai, D.,
Domain Adaptation for Semantic Segmentation With Maximum Squares Loss,
ICCV19(2090-2099)
IEEE DOI
Code, Domain Adaption.
WWW Link. entropy, image segmentation, minimisation, neural nets, supervised learning, semantic segmentation, maximum squares loss, Training

Hao, F.S.[Fu-Sheng], He, F.X.[Feng-Xiang], Cheng, J.[Jun], Wang, L., Cao, J., Tao, D.C.[Da-Cheng],
Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning,
ICCV19(8459-8468)
IEEE DOI
Code, Metric Learning.
WWW Link. image retrieval, learning (artificial intelligence), multilayer perceptrons, tensors, 3D tensor, Task analysis

Chen, J.Z.[Jing-Zhou], Wang, P.[Peng], Liu, J.[Jian], Qian, Y.T.[Yun-Tao],
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification,
CVPR22(4848-4857)
IEEE DOI
Code, Classification.
WWW Link. Image quality, Representation learning, Knowledge engineering, Machine vision, Semantics, Probabilistic logic, Vision applications and systems

Saito, K., Kim, D., Sclaroff, S., Darrell, T.J., Saenko, K.,
Semi-Supervised Domain Adaptation via Minimax Entropy,
ICCV19(8049-8057)
IEEE DOI
Code, Domain Adaption.
HTML Version. convolutional neural nets, entropy, feature extraction, minimax techniques, pattern classification, supervised learning, Computational modeling

Liu, B., Wu, Z., Hu, H., Lin, S.,
Deep Metric Transfer for Label Propagation with Limited Annotated Data,
MDALC19(1317-1326)
IEEE DOI
Code, Learning.
WWW Link. image annotation, object recognition, unsupervised learning, deep metric transfer, limited annotated data, object class, self supervised learning

Cohen, T.[Tomer], Wolf, L.B.[Lior B.],
Bidirectional One-Shot Unsupervised Domain Mapping,
ICCV19(1784-1792)
IEEE DOI
Code, Learning.
WWW Link. image processing, unsupervised learning, single sample domain, bidirectional one-shot unsupervised domain mapping, Unsupervised learning

Lee, S., Kim, D., Kim, N., Jeong, S.,
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation,
ICCV19(91-100)
IEEE DOI
Code, Domain Adaption.
WWW Link. feature extraction, image classification, image representation, image segmentation, unsupervised learning, Neurons

Xu, R., Li, G., Yang, J., Lin, L.,
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation,
ICCV19(1426-1435)
IEEE DOI
Code, Domain Adaption.
WWW Link. learning (artificial intelligence), task-specific features, standard domain adaptation, partial domain adaptation, Neural networks

Gidaris, S., Bursuc, A., Komodakis, N., Pérez, P.P., Cord, M.,
Boosting Few-Shot Visual Learning With Self-Supervision,
ICCV19(8058-8067)
IEEE DOI
Code, Learning.
WWW Link. feature extraction, image representation, learning (artificial intelligence), neural nets, Feature extraction

Elhoseiny, M., Elfeki, M.,
Creativity Inspired Zero-Shot Learning,
ICCV19(5783-5792)
IEEE DOI
Code, Learning.
WWW Link. image denoising, image recognition, learning (artificial intelligence), psychology, text analysis, ZSL, Encyclopedias

Georgescu, B.[Bogdan],
HEIV based estimation,
OnlineSeptember, 2002. Code, HEIV.
WWW Link. Code related to above paper.
See also Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications.

Presto-Box: Pattern REcognition Scilab TOolBOX,
2001
HTML Version. Code, Pattern Recognition. Routines for students to experiment with basic pattern recognition principles.

PRTools: The Matlab Toolbox for Pattern Recognition,
2004.
WWW Link. Code, Pattern Recognition. Matlab package for PR Implementation based on the book:
See also Classification, parameter estimation and state estimation: An engineering approach using Matlab.

MultiSpec: A Freeware Multispectral Image Data Analysis System,
2007.
WWW Link. Code, Pattern Recognition. Purdue package for analyzing multispectral and hyperspectral data.
See also Purdue University.

van Gansbeke, W.[Wouter], Vandenhende, S.[Simon], Georgoulis, S.[Stamatios], Proesmans, M.[Marc], Van Gool, L.J.[Luc J.],
Scan: Learning to Classify Images Without Labels,
ECCV20(X:268-285).
Springer DOI
Code, Classification.
WWW Link.

Newell, A.[Alejandro], Deng, J.[Jia],
How Useful Is Self-Supervised Pretraining for Visual Tasks?,
CVPR20(7343-7352)
IEEE DOI
Code, Pretraining.
WWW Link. Task analysis, Training, Image color analysis, Data models, Complexity theory, Visualization, Benchmark testing

Wang, Q., Li, W., Van Gool, L.J.,
Semi-Supervised Learning by Augmented Distribution Alignment,
ICCV19(1466-1475)
IEEE DOI
Code, Learning.
WWW Link. interpolation, neural nets, supervised learning, semisupervised learning approach, unlabeled data, Benchmark testing

Carreira-Perpiñán, M.Á.[Miguel Á.],
Mode-Finding for Mixtures of Gaussian Distributions,
PAMI(22), No. 11, November 2000, pp. 1318-1323.
IEEE DOI
Matlab implementation and TR with mathematical details:
HTML Version. Code, Modes.

Ko, D.[Dohwan], Choi, J.[Joonmyung], Ko, J.[Juyeon], Noh, S.[Shinyeong], On, K.W.[Kyoung-Woon], Kim, E.S.[Eun-Sol], Kim, H.W.J.[Hyun-Woo J.],
Video-Text Representation Learning via Differentiable Weak Temporal Alignment,
CVPR22(5006-5015)
IEEE DOI
Code, Contrastive Learning.
WWW Link. Representation learning, Codes, Computational modeling, Self-supervised learning, Data models, Pattern recognition, Self- semi- meta- Video analysis and understanding

Jia, L.L.[Lin-Lin], Gaüzère, B.[Benoit], Honeine, P.[Paul],
graphkit-learn: A Python library for graph kernels based on linear patterns,
PRL(143), 2021, pp. 113-121.
Elsevier DOI
Code, Graph Kernel. Graph kernels, Linear patterns, Python implementation

Yu, S.[Shi], Tranchevent, L.[Leon], Liu, X.H.[Xin-Hai], Glanzel, W.[Wolfgang], Suykens, J.A.K.[Johan A.K.], de Moor, B.[Bart], Moreau, Y.[Yves],
Optimized Data Fusion for Kernel k-Means Clustering,
PAMI(34), No. 5, May 2012, pp. 1031-1039.
IEEE DOI
Combine multiple data sources for k-means. Code, Clustering. Code:
HTML Version.

Chang, C.C., Lin, C.J.,
LIBSVM: a library for support vector machines,
Online2001.
WWW Link. Code, Support Vector Machines.

LIBSVMTL: a Support Vector Machine Template Library,
Online2001.
HTML Version. Code, Support Vector Machines. Based on LIBSVM above.

Marques F., P.C.[Paulo C.],
Confidence intervals for the random forest generalization error,
PRL(158), 2022, pp. 171-175.
Elsevier DOI
WWW Link. Code, Random Forest. Random forests, Generalization error, Out-of-bag estimation, Confidence interval, Bootstrapping

Torch: Machine-Learning Library,
2004.
WWW Link. Code, Learning. Open source learning library.

Xie, J.H.[Jia-Hao], Zhan, X.H.[Xiao-Hang], Liu, Z.W.[Zi-Wei], Ong, Y.S.[Yew-Soon], Loy, C.C.[Chen Change],
Delving into Inter-Image Invariance for Unsupervised Visual Representations,
IJCV(130), No. 12, December 2022, pp. 2994-3013.
Springer DOI
Code, Learning.
WWW Link.

Goyal, P.[Priya], Mahajan, D.[Dhruv], Gupta, A.[Abhinav], Misra, I.[Ishan],
Scaling and Benchmarking Self-Supervised Visual Representation Learning,
ICCV19(6390-6399)
IEEE DOI
Code, Learning.
WWW Link. image representation, object detection, supervised learning, self-supervised learning, Navigation

Open Deep Learning Toolkit for Robotics (OpenDR),
2021.
WWW Link.
WWW Link.
WWW Link. Code, Deep Learning.
WWW Link.
Updated to version 3.0, with more features. The toolkit provides more than 20 methods, for human pose estimation, face detection, recognition, facial expression recognition, semantic and panoptic segmentation, video and skeleton-based action recognition, image, multimodal and point cloud-based object detection, 2D and 3D object tracking, speech command recognition, heart anomaly detection, navigation for wheeled robots, and grasping.

Deep Learning Tool Kit for Medical Imaging,
2017.
WWW Link. Code, Neural Networks. Neural networks toolkit written in python, on top of Tensorflow. Its modular architecture was developed to enable fast prototyping and ensure reproducibility in image analysis applications, with a particular focus on medical imaging.

Pandya, A.S.[Abhijit S.], Macy, R.B.[Robert B.],
Pattern Recognition with Neural Networks in C++,
CRC PressBoca Raton, FL. 1996. Code, Neural Networks. ISBN 0-8493-9462-7. Complete code for the various algorithms.

Handa, A.[Ankur], Bloesch, M.[Michael], Patraucean, V.[Viorica], Stent, S.[Simon], McCormac, J.[John], Davison, A.[Andrew],
gvnn: Neural Network Library for Geometric Computer Vision,
DeepLearn16(III: 67-82).
Springer DOI
Code, Neural Networks.

Qin, A.K., Suganthan, P.N.,
Enhanced neural gas network for prototype-based clustering,
PR(38), No. 8, August 2005, pp. 1275-1288.
Elsevier DOI
Code, Neural Networks.
Earlier:
Kernel neural gas algorithms with application to cluster analysis,
ICPR04(IV: 617-620).
IEEE DOI
Code available:
WWW Link.

Shi, Z.L.[Zeng-Lin], Mettes, P.S.[Pascal S.], Maji, S.[Subhransu], Snoek, C.G.M.[Cees G. M.],
On Measuring and Controlling the Spectral Bias of the Deep Image Prior,
IJCV(130), No. 1, January 2022, pp. 885-908.
Springer DOI
Code, Spectal Bias.
WWW Link.

Gong, X.Y.[Xin-Yu], Chen, W.Y.[Wu-Yang], Chen, T.L.[Tian-Long], Wang, Z.Y.[Zhang-Yang],
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity,
WACV22(2957-2967)
IEEE DOI
Code, Normalization.
WWW Link. Build on DARTS. Training, Codes, Image synthesis, Semisupervised learning, Data models, Robustness, Deep Learning

Gong, X.Y.[Xin-Yu], Chang, S.Y.[Shi-Yu], Jiang, Y.F.[Yi-Fan], Wang, Z.Y.[Zhang-Yang],
AutoGAN: Neural Architecture Search for Generative Adversarial Networks,
ICCV19(3223-3233)
IEEE DOI
Code, Generative Adversarial Network.
WWW Link. image classification, image segmentation, neural nets, neural architecture search, generative adversarial networks, Prediction algorithms

Chen, X.[Xin], Xie, L.X.[Ling-Xi], Wu, J.[Jun], Tian, Q.[Qi],
Progressive Differentiable Architecture Search: Bridging the Depth Gap Between Search and Evaluation,
ICCV19(1294-1303)
IEEE DOI
Code, Search.
WWW Link. approximation theory, image recognition, learning (artificial intelligence), neural net architecture, Computational modeling

Yan, S., Fang, B., Zhang, F., Zheng, Y., Zeng, X., Zhang, M., Xu, H.,
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking,
NeruArch19(1942-1950)
IEEE DOI
Code, Neural Netowrks.
WWW Link. learning (artificial intelligence), neural net architecture, multilevel architecture, flexible network architectures, Hierarchical Masking

Zheng, X., Ji, R., Tang, L., Zhang, B., Liu, J., Tian, Q.,
Multinomial Distribution Learning for Effective Neural Architecture Search,
ICCV19(1304-1313)
IEEE DOI
Code, Neural Networks.
WWW Link. graphics processing units, learning (artificial intelligence), neural nets, Search problems

Wang, Z., Zou, W., Xu, C.,
PR Product: A Substitute for Inner Product in Neural Networks,
ICCV19(6012-6021)
IEEE DOI
Code, Neural Netowrks.
WWW Link. convolutional neural nets, image classification, learning (artificial intelligence), recurrent neural nets, Computational modeling

Taha, A.[Ahmed], Chen, Y.T.[Yi-Ting], Misu, T.[Teruhisa], Shrivastava, A.[Abhinav], Davis, L.S.[Larry S.],
Boosting Standard Classification Architectures Through a Ranking Regularizer,
WACV20(747-755)
IEEE DOI
Code, Classification.
WWW Link. Standards, Head, Magnetic losses, Magnetic separation, Visualization, Magnetic heads

Ma, N.N.[Ning-Ning], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
Funnel Activation for Visual Recognition,
ECCV20(XI:351-368).
Springer DOI
Code, Image Recognition.
WWW Link. Extends ReLU and PReLU to a 2D activation.

Caron, M., Bojanowski, P., Mairal, J., Joulin, A.,
Unsupervised Pre-Training of Image Features on Non-Curated Data,
ICCV19(2959-2968)
IEEE DOI
Code, Learning.
WWW Link. convolutional neural nets, image classification, unsupervised learning, unsupervised methods, Standards

Wu, H.P.[Hai-Ping], Xiao, B.[Bin], Codella, N.[Noel], Liu, M.C.[Meng-Chen], Dai, X.Y.[Xi-Yang], Yuan, L.[Lu], Zhang, L.[Lei],
CvT: Introducing Convolutions to Vision Transformers,
ICCV21(22-31)
IEEE DOI
Code, Vision Transformer.
WWW Link. Convolutional codes, Image resolution, Image recognition, Performance gain, Transformers, Distortion,

Huang, S.H.[Shi-Hua], Lu, Z.C.[Zhi-Chao], Cheng, R.[Ran], He, C.[Cheng],
FaPN: Feature-aligned Pyramid Network for Dense Image Prediction,
ICCV21(844-853)
IEEE DOI
Code, Deep Learning.
WWW Link. Deep learning, Image segmentation, Codes, Neural networks, Feature extraction, grouping and shape

Zhang, X.[Xiao], Zhao, R.[Rui], Qiao, Y.[Yu], Li, H.S.[Hong-Sheng],
RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax,
ECCV20(XXVI:296-311).
Springer DOI
Code, RBF.
WWW Link.

Chen, D.D.[Dong-Dong], Davies, M.E.[Mike E.],
Deep Decomposition Learning for Inverse Imaging Problems,
ECCV20(XXVIII:510-526).
Springer DOI
Code, DNN.
WWW Link.

Pan, X.[Xuran], Ge, C.J.[Chun-Jiang], Lu, R.[Rui], Song, S.[Shiji], Chen, G.F.[Guan-Fu], Huang, Z.Y.[Ze-Yi], Huang, G.[Gao],
On the Integration of Self-Attention and Convolution,
CVPR22(805-815)
IEEE DOI
Code, Representation Learning.
WWW Link. Representation learning, Image recognition, Convolution, Computational modeling, Object detection, Pattern recognition, Representation learning

Han, K.[Kai], Wang, Y.H.[Yun-He], Xu, C.[Chang], Guo, J.Y.[Jian-Yuan], Xu, C.J.[Chun-Jing], Wu, E.[Enhua], Tian, Q.[Qi],
GhostNets on Heterogeneous Devices via Cheap Operations,
IJCV(130), No. 1, January 2022, pp. 1050-1069.
Springer DOI
Code, Neural Networks.
WWW Link.
WWW Link.

Yang, T.J.N.[Tao-Jian-Nan], Zhu, S.J.[Si-Jie], Chen, C.[Chen], Yan, S.[Shen], Zhang, M.[Mi], Willis, A.[Andrew],
MutualNet: Adaptive Convnet via Mutual Learning from Network Width and Resolution,
ECCV20(I:299-315).
Springer DOI
Code, ConvNet.
WWW Link. Executable with dynamic resources.

Kehrenberg, T.[Thomas], Bartlett, M.[Myles], Thomas, O.[Oliver], Quadrianto, N.[Novi],
Null-sampling for Interpretable and Fair Representations,
ECCV20(XXVI:565-580).
Springer DOI
Code, CNN.
WWW Link.

Vooturi, D.T.[Dharma Teja], Varma, G.[Girish], Kothapalli, K.[Kishore],
Dynamic Block Sparse Reparameterization of Convolutional Neural Networks,
CEFRL19(3046-3053)
IEEE DOI
Code, Convolutional Networks.
WWW Link. convolutional neural nets, image classification, learning (artificial intelligence), dense neural networks, neural networks

Yan, X.P.[Xiao-Peng], Chen, Z.L.[Zi-Liang], Xu, A.[Anni], Wang, X.X.[Xiao-Xi], Liang, X.D.[Xiao-Dan], Lin, L.[Liang],
Meta R-CNN: Towards General Solver for Instance-Level Low-Shot Learning,
ICCV19(9576-9585)
IEEE DOI
Code, Learning.
HTML Version. convolutional neural nets, image representation, image sampling, image segmentation, Object recognition

Kobayashi, T.[Takumi],
t-vMF Similarity For Regularizing Intra-Class Feature Distribution,
CVPR21(6612-6621)
IEEE DOI
WWW Link. Code, Training. Training, Computational modeling, Focusing, Pattern recognition, Noise measurement, Convolutional neural networks

Rodríguez, P.[Pau], Caccia, M.[Massimo], Lacoste, A.[Alexandre], Zamparo, L.[Lee], Laradji, I.[Issam], Charlin, L.[Laurent], Vazquez, D.[David],
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations,
ICCV21(1036-1045)
IEEE DOI
Code, Explaination.
WWW Link. Codes, Computational modeling, Perturbation methods, Decision making, Machine learning, Predictive models, and ethics in vision

Xu, S.[Shawn], Venugopalan, S.[Subhashini], Sundararajan, M.[Mukund],
Attribution in Scale and Space,
CVPR20(9677-9686)
IEEE DOI
Code, Deep Nets.
WWW Link. Perturbation methods, Task analysis, Kernel, Mathematical model, Google, Medical services

Wu, T., Song, X.,
Towards Interpretable Object Detection by Unfolding Latent Structures,
ICCV19(6032-6042)
IEEE DOI
Code, Object Detection.
WWW Link. convolutional neural nets, grammars, learning (artificial intelligence), object detection, Predictive models

Zhuang, J., Dvornek, N.C., Li, X., Yang, J., Duncan, J.,
Decision explanation and feature importance for invertible networks,
VXAI19(4235-4239)
IEEE DOI
Code, Neural Networks.
WWW Link. neural nets, pattern classification, linear classifier, feature space, decision boundary, feature importance, Decision-Boundary

Lee, K., Lee, K., Shin, J., Lee, H.,
Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild,
ICCV19(312-321)
IEEE DOI
Code, Neural Networks.
WWW Link. image sampling, learning (artificial intelligence), neural nets, distillation loss, global distillation, learning strategy, Neural networks

Schaefer, S.[Simon], Gehrig, D.[Daniel], Scaramuzza, D.[Davide],
AEGNN: Asynchronous Event-based Graph Neural Networks,
CVPR22(12361-12371)
IEEE DOI
Code, GNN.
WWW Link. Power demand, Object detection, Market research, Graph neural networks, Pattern recognition, Object recognition, Scene analysis and understanding

McKernel: A Library for Approximate Kernel Expansions in Log-linear Time,
2020.
WWW Link.
PDF File. Code, Neural Networks. Code, Kernel Expansion.

Wang, J.[Jiakai], Yin, Z.X.[Zi-Xin], Hu, P.F.[Peng-Fei], Liu, A.[Aishan], Tao, R.S.[Ren-Shuai], Qin, H.T.[Hao-Tong], Liu, X.L.[Xiang-Long], Tao, D.C.[Da-Cheng],
Defensive Patches for Robust Recognition in the Physical World,
CVPR22(2446-2455)
IEEE DOI
Code, Deep Learning.
WWW Link. Deep learning, Visualization, Snow, Machine vision, Urban areas, Robustness, Vision applications and systems, Computer vision for social good

Gai, S.[Shan], Huang, X.[Xiang],
Reduced Biquaternion Convolutional Neural Network for Color Image Processing,
CirSysVideo(32), No. 3, March 2022, pp. 1061-1075.
IEEE DOI
WWW Link. Code, CNN. Algebra, Color, Quaternions, Convolutional neural networks, Neural networks, Feature extraction, Convolution, color image classification

Zhao, R.Z.[Rong-Zhen], Li, J.[Jian], Wu, Z.Z.[Zhen-Zhi],
Convolution of Convolution: Let Kernels Spatially Collaborate,
CVPR22(641-650)
IEEE DOI
Code, CNN.
WWW Link. Training, Visualization, Convolution, Neurons, Performance gain, Retina, Pattern recognition, grouping and shape analysis

Huang, G.X.[Guo-Xi], Bors, A.G.[Adrian G.],
Busy-Quiet Video Disentangling for Video Classification,
WACV22(756-765)
IEEE DOI

Earlier:
Region-based Non-local Operation for Video Classification,
ICPR21(10010-10017)
IEEE DOI
Code, Classification.
WWW Link. Band-pass filters, Representation learning, Codes, Frequency-domain analysis, Computational modeling, Redundancy, Object Detection/Recognition/Categorization. Integrate into existing CNN framework. Training, Convolution, Stacking, Benchmark testing, Convolutional neural networks, Optimization

Ma, N.N.[Ning-Ning], Zhang, X.Y.[Xiang-Yu], Huang, J.W.[Jia-Wei], Sun, J.[Jian],
WeightNet: Revisiting the Design Space of Weight Networks,
ECCV20(XV:776-792).
Springer DOI
Code, Neural Nets.
WWW Link. Unifies two current distinct and extremely effective SENet and CondConv.

Li, D.[Duo], Yao, A.B.[An-Bang], Chen, Q.F.[Qi-Feng],
PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer,
ECCV20(XXI:615-632).
Springer DOI
Code, CNN.
WWW Link.

Huh, M.Y.[Min-Young], Zhang, R.[Richard], Zhu, J.Y.[Jun-Yan], Paris, S.[Sylvain], Hertzmann, A.[Aaron],
Transforming and Projecting Images into Class-conditional Generative Networks,
ECCV20(II:17-34).
Springer DOI
Code, GAN.
WWW Link.

Li, D.[Duo], Yao, A.B.[An-Bang], Chen, Q.F.[Qi-Feng],
Learning to Learn Parameterized Classification Networks for Scalable Input Images,
ECCV20(XXIX: 19-35).
Springer DOI
Code, CNN.
WWW Link. CNNs don't do well with resolution changes.

Li, D., Zhou, A., Yao, A.,
HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions,
ICCV19(3315-3324)
IEEE DOI
Code, Convolutional Neural Nets.
WWW Link. convolutional neural nets, feature extraction, image classification, image representation, object detection, Tensile stress

Huang, Y., Ou, P., Wu, R., Feng, Z.,
Sequentially Aggregated Convolutional Networks,
NeruArch19(1900-1909)
IEEE DOI
Code Convolutional Networks.
WWW Link. convolutional neural nets, image classification, learning (artificial intelligence), optimisation, Image classification

Shen, Z.Q.[Zhi-Qiang], Liu, Z.[Zechun], Qin, J.[Jie], Huang, L.[Lei], Cheng, K.T.[Kwang-Ting], Savvides, M.[Marios],
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration,
CVPR21(2165-2174)
IEEE DOI
WWW Link. Code, Learning. Training, Degradation, Codes, Neural networks, Supervised learning, Predictive models

Yu, J., Huang, T.,
Universally Slimmable Networks and Improved Training Techniques,
ICCV19(1803-1811)
IEEE DOI
Code, Neural Networks.
WWW Link. image classification, image resolution, learning (artificial intelligence), mobile computing, Testing

Wang, Y.K.[Yi-Kai], Sun, F.C.[Fu-Chun], Li, D.[Duo], Yao, A.B.[An-Bang],
Resolution Switchable Networks for Runtime Efficient Image Recognition,
ECCV20(XV:533-549).
Springer DOI
Code, Network Pruning.
WWW Link. Limit the network to vary image resolution and computation time.

Messikommer, N.[Nico], Gehrig, D.[Daniel], Loquercio, A.[Antonio], Scaramuzza, D.[Davide],
Event-based Asynchronous Sparse Convolutional Networks,
ECCV20(VIII:415-431).
Springer DOI
WWW Link. Code, Semantic Segmentation.
WWW Link. Dataset, Semantic Segmentation.

Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K., Sun, J.,
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning,
ICCV19(3295-3304)
IEEE DOI
Code, Neural Networks.
WWW Link. learning (artificial intelligence), neural nets, sampling methods, stochastic processes, pruned networks, Task analysis

Li, Y., Chen, Y., Wang, N., Zhang, Z.,
Scale-Aware Trident Networks for Object Detection,
ICCV19(6053-6062)
IEEE DOI
Code, Object Detection.
WWW Link. feature extraction, learning (artificial intelligence), neural nets, Computer architecture

Tian, Z., Shen, C., Chen, H., He, T.,
FCOS: Fully Convolutional One-Stage Object Detection,
ICCV19(9626-9635)
IEEE DOI
Code, Object Detection.
WWW Link. image segmentation, object detection, predefined anchor boxes, final detection performance, pre-defined set, anchor box free, Head

Nie, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.,
Enriched Feature Guided Refinement Network for Object Detection,
ICCV19(9536-9545)
IEEE DOI
Code, Object Detection.
WWW Link. feature extraction, image classification, learning (artificial intelligence), neural nets, Benchmark testing

Wang, T., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., Shao, L.,
Learning Rich Features at High-Speed for Single-Shot Object Detection,
ICCV19(1971-1980)
IEEE DOI
Code, Object Detection.
WWW Link. image classification, image representation, learning (artificial intelligence), object detection, Training

Liu, A.S.[Ai-Shan], Huang, T.R.[Tai-Ran], Liu, X.L.[Xiang-Long], Xu, Y.T.[Yi-Tao], Ma, Y.Q.[Yu-Qing], Chen, X.Y.[Xin-Yun], Maybank, S.J.[Stephen J.], Tao, D.C.[Da-Cheng],
Spatiotemporal Attacks for Embodied Agents,
ECCV20(XVII:122-138).
Springer DOI
Code, Adversarial Attack.
WWW Link.

Sun, W.[Wei], Wu, T.F.[Tian-Fu],
Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis,
PAMI(44), No. 9, September 2022, pp. 5070-5087.
IEEE DOI

Earlier:
Image Synthesis From Reconfigurable Layout and Style,
ICCV19(10530-10539)
IEEE DOI
Code, Image Synthesis.
WWW Link. Layout, Image synthesis, Generators, Snow, Task analysis, Training, Fasteners, Image synthesis, layout-to-image, ISLA-norm. genomics, learning (artificial intelligence), LostGAN, reconfigurable layout, fine-grained mask maps, Image resolution

Sushko, V.[Vadim], Schönfeld, E.[Edgar], Zhang, D.[Dan], Gall, J.[Juergen], Schiele, B.[Bernt], Khoreva, A.[Anna],
OASIS: Only Adversarial Supervision for Semantic Image Synthesis,
IJCV(130), No. 12, December 2022, pp. 2903-2923.
Springer DOI
Code, Image Synthesis.
WWW Link.

Lee, W.[Wonkwang], Kim, D.[Donggyun], Hong, S.[Seunghoon], Lee, H.L.[Hong-Lak],
High-fidelity Synthesis with Disentangled Representation,
ECCV20(XXVI:157-174).
Springer DOI
Code, Image Synthesis.
WWW Link.

Yang, L., Cheung, N., Li, J., Fang, J.,
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding,
ICCV19(6439-6448)
IEEE DOI
Code, Graph Embedding.
WWW Link. data structures, feature extraction, Gaussian processes, graph theory, learning (artificial intelligence), minimisation, Gaussian mixture model

Beveridge, J.R.[J. Ross], Bolme, D.S.[David S.], Draper, B.A.[Bruce A.], Teixeira, M.[Marcio],
The CSU Face Identification Evaluation System: Its purpose, features, and structure,
MVA(16), No. 2, February 2005, pp. 128-138.
Springer DOI
Evaluation, Faces.
Earlier: A2, A1, A4, A3: CVS03(304 ff).
Springer DOI
HTML Version.
Code, Face Recognition. The four algorithms provided are principle components analysis (PCA), a.k.a eigenfaces (
See also Eigenfaces for Recognition. ), a combined principle components analysis and linear discriminant analysis algorithm (PCA + LDA) (
See also Discriminant Analysis of Principal Components for Face Recognition. ), an intrapersonal/extrapersonal image difference classifier (IIDC), (
See also Bayesian similarity measure for deformable image matching, A. ) and an elastic bunch graph matching (EBGM) algorithm (
See also Face Recognition by Elastic Bunch Graph Matching. ) The PCA + LDA, IIDC, and EBGM algorithms are based upon algorithms used in the FERET study (
See also FERET Evaluation Methodology for Face-Recognition Algorithms, The. ).

Xu, X., Kakadiaris, I.A.,
FaRE: Open Source Face Recognition Performance Evaluation Package,
ICIP19(3272-3276)
IEEE DOI
Code, Face Recognition. Face Recognition, Evaluation, Toolbox

Face Recogniton Home Page,
Online2006.
WWW Link. Code, Face Recognition. Dataset, Faces. Listing of research groups, databases, and vendors.

Face Detection Home Page,
Online2007.
WWW Link. Code, Face Detection. Dataset, Faces. Listing of research groups, databases, and vendors.

Hu, W.[Wei], Huang, Y.Y.[Yang-Yu], Zhang, F.[Fan], Li, R.R.[Rui-Rui], Li, H.C.[Heng-Chao],
SeqFace: Learning discriminative features by using face sequences,
IET-IPR(15), No. 11, 2021, pp. 2548-2558.
DOI Link
WWW Link. Code, Face Recognition. Training on face sequences from video. CNNs, face recognition, face sequences, training data augmentation

Thewlis, J.[James], Albanie, S.[Samuel], Bilen, H.[Hakan], Vedaldi, A.[Andrea],
Unsupervised Learning of Landmarks by Descriptor Vector Exchange,
ICCV19(6360-6370)
IEEE DOI
Code, Landmarks.
WWW Link. feature extraction, image matching, unsupervised learning, unsupervised learning, descriptor vector exchange, Feature extraction

Sun, K., Wu, W., Liu, T., Yang, S., Wang, Q., Zhou, Q., Ye, Z., Qian, C.,
FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos,
ICCV19(5461-5470)
IEEE DOI
Code, Facial Landmarks.
HTML Version. face recognition, image restoration, object detection, video signal processing, Geometry

Qian, S., Sun, K., Wu, W., Qian, C., Jia, J.,
Aggregation via Separation: Boosting Facial Landmark Detector With Semi-Supervised Style Translation,
ICCV19(10152-10162)
IEEE DOI
Code, Facial Landmarks.
WWW Link. face recognition, image texture, object detection, supervised learning, facial landmark detector, Image reconstruction

Merget, D., Eckl, T., Schwoerer, M., Tiefenbacher, P., Rigoll, G.,
Capturing facial videos with Kinect 2.0: A multithreaded open source tool and database,
WACV16(1-5)
IEEE DOI
Code, Face Analysis. Databases

TLD: Tracks the object, Learns its appearance and Detects,
2011
HTML Version. Code, Face Tracking. Code, Face Detection.
See also University of Surrey.

Wang, Y.Q.[Yi-Qing],
An Analysis of the Viola-Jones Face Detection Algorithm,
IPOL(2014), No. 2014, pp. 128-148.
DOI Link
Code, Face Detection.
See also Robust Real-Time Face Detection.

Lisani, J.L.[Jose-Luis], Ramis, S.[Silvia],
A Contrario Detection of Faces with a Short Cascade of Classifiers,
IPOL(9), 2019, pp. 269-290.
DOI Link
Code, Face Detection.
See also Contrario Detection of Faces: A Case Example, A.

Wang, M.[Meng], Guo, X.J.[Xiao-Jie], Dai, W.J.[Wen-Jing], Zhang, J.[Jiawan],
Face Inverse Rendering via Hierarchical Decoupling,
IP(31), 2022, pp. 5748-5761.
IEEE DOI
Code, Face Relighting.
WWW Link. Faces, Lighting, Rendering (computer graphics), Image decomposition, Image reconstruction, Task analysis, deep learning

Zhou, H., Hadap, S., Sunkavalli, K., Jacobs, D.,
Deep Single-Image Portrait Relighting,
ICCV19(7193-7201)
IEEE DOI
Code, Relighting.
HTML Version. convolutional neural nets, face recognition, image enhancement, image resolution, lighting, rendering (computer graphics), Feature extraction
See also Experiments on Deep Single-Image Portrait Relighting.

de la Torre, F.[Fernando], Chu, W.S.[Wen-Sheng], Xiong, X.H.[Xue-Han], Vicente, F., Ding, X.Y.[Xiao-Yu], Cohn, J.F.,
IntraFace,
FG15(1-8)
IEEE DOI
Code, Feature Tracking.
WWW Link. Facial feature tracking. computer vision.

La Cascia, M.[Marco], Sclaroff, S.[Stan], Athitsos, V.[Vassilis],
Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Textured-Mapped 3D Models,
PAMI(22), No. 4, April 2000, pp. 322-336.
IEEE DOI
PDF File.
Code, Head Tracking.
HTML Version. Model the head as a texture mapped cylinder.
See also Skin Color-Based Video Segmentation under Time-Varying Illumination.

openEyes,
2006.
WWW Link. Code, Eye Tracking. Open source code for eye tracking.
See also Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches.

PeyeMMV.,
2023
WWW Link. Code, Eye Fixation.
Elsevier DOI Python module called PeyeMMV. PeyeMMV implements the two-step spatial dispersion fixation detection algorithm imported in both EyeMMV and LandRate MATLAB toolboxes.

Park, S., Mello, S.D., Molchanov, P., Iqbal, U., Hilliges, O., Kautz, J.,
Few-Shot Adaptive Gaze Estimation,
ICCV19(9367-9376)
IEEE DOI
Code, Gaze.
WWW Link. gaze tracking, learning (artificial intelligence), motion estimation, neural net architecture, Robustness

Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., Torralba, A.,
Gaze360: Physically Unconstrained Gaze Estimation in the Wild,
ICCV19(6911-6920)
IEEE DOI
Code, Gaze.
WWW Link. gaze tracking, pose estimation, stereo image processing, gaze benchmark datasets, cross-dataset domain adaptation, Lighting

Bishay, M.[Mina], Preston, K.[Kenneth], Strafuss, M.[Matthew], Page, G.[Graham], Turcot, J.[Jay], Mavadati, M.[Mohammad],
AFFDEX 2.0: A Real-Time Facial Expression Analysis Toolkit,
FG23(1-8)
IEEE DOI
Code, Facial Expressions. Measurement, Gold, Emotion recognition, Linux, Face recognition, Gesture recognition

Littlewort, G.C.[Gwen C.], Whitehill, J.[Jacob], Wu, T.F.[Ting-Fan], Fasel, I.R.[Ian R.], Frank, M.[Mark], Movellan, J.R.[Javier R.], Bartlett, M.S.[Marian Stewart],
The computer expression recognition toolbox (CERT),
FG11(298-305).
IEEE DOI
Code, Facial Expressions.
See also motion in emotion: A CERT based approach to the FERA emotion challenge, The.

Bartlett, M.S.[Marian S.], Littlewort, G.C.[Gwen C.], Wu, T.F.[Ting-Fan], Movellan, J.R.[Javier R.],
Computer Expression Recognition Toolbox,
FG08(1-2).
IEEE DOI
Code, Facial Expressions.

Buitelaar, P., Wood, I.D., Negi, S., Arcan, M., McCrae, J.P., Abele, A., Robin, C., Andryushechkin, V., Ziad, H., Sagha, H., Schmitt, M., Schuller, B.W., Sánchez-Rada, J.F., Iglesias, C.A., Navarro, C., Giefer, A., Heise, N., Masucci, V., Danza, F.A., Caterino, C., Smrž, P., Hradiš, M., Povolný, F., Klimeš, M., Matejka, P., Tummarello, G.,
MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,
MultMed(20), No. 9, September 2018, pp. 2454-2465.
IEEE DOI
Code, Emotion Analysis. emotion recognition, face recognition, pose estimation, text analysis, assistive technologies, call-centre operations, video processing

Baltrušaitis, T.[Tadas], Zadeh, A., Lim, Y.C., Morency, L.P.[Louis-Philippe],
OpenFace 2.0: Facial Behavior Analysis Toolkit,
FG18(59-66)
IEEE DOI
Code, Face Analysis. Estimation, Face, Magnetic heads, Real-time systems, Tools, Training, eye gaze, facial behavior analysis, head pose, landmark detection

Baltrušaitis, T.[Tadas], Robinson, P.[Peter], Morency, L.P.[Louis-Philippe],
OpenFace: An open source facial behavior analysis toolkit,
WACV16(1-10)
IEEE DOI
Code, Face Analysis. Estimation; Face; Magnetic heads; Real-time systems; Training; Videos
See also eNTERFACE-05 Audio-Visual Emotion Database, The.

Pytel, R.[Rafal], Kayhan, O.S.[Osman Semih], van Gemert, J.C.[Jan C.],
Tilting at windmills: Data augmentation for deep pose estimation does not help with occlusions,
ICPR21(10568-10575)
IEEE DOI
Code, Human Pose.
WWW Link. Occlusions degrade performance. Pose estimation

Zhou, K.[Keyang], Bhatnagar, B.L.[Bharat Lal], Pons-Moll, G.[Gerard],
Unsupervised Shape and Pose Disentanglement for 3d Meshes,
ECCV20(XXII:341-357).
Springer DOI
Code, Pose Estimation.
WWW Link.

Hassan, M., Choutas, V., Tzionas, D., Black, M.,
Resolving 3D Human Pose Ambiguities With 3D Scene Constraints,
ICCV19(2282-2292)
IEEE DOI
Code, Human Pose.
WWW Link. image motion analysis, image reconstruction, image sequences, pose estimation, 3D human pose ambiguities, scene constraints,

Kolotouros, N., Pavlakos, G., Black, M., Daniilidis, K.,
Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop,
ICCV19(2252-2261)
IEEE DOI
Code, Human Pose.
WWW Link. image motion analysis, image reconstruction, iterative methods, learning (artificial intelligence), optimisation, Pose estimation

Pavlakos, G.[Georgios], Kolotouros, N.[Nikos], Daniilidis, K.[Kostas],
TexturePose: Supervising Human Mesh Estimation With Texture Consistency,
ICCV19(803-812)
IEEE DOI
Code, Mesh.
WWW Link.
Earlier: A2, A1, A3:
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction,
CVPR19(4496-4505).
IEEE DOI
cameras, image sequences, image texture, pose estimation, video signal processing, natural images, network architecture, Parametric statistics

Sharma, S., Varigonda, P.T., Bindal, P., Sharma, A., Jain, A.,
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking,
ICCV19(2325-2334)
IEEE DOI
Code, Human Pose.
WWW Link. learning (artificial intelligence), neural nets, pose estimation, solid modelling, stereo image processing, Heating systems

Martinez, G.H., Raaj, Y., Idrees, H., Xiang, D., Joo, H., Simon, T., Sheikh, Y.,
Single-Network Whole-Body Pose Estimation,
ICCV19(6981-6990)
IEEE DOI
Code, Human Pose.
WWW Link. computational complexity, face recognition, image resolution, learning (artificial intelligence), pose estimation

Sidenbladh, H.[Hedvig], Black, M.J.[Michael J.],
Learning the Statistics of People in Images and Video,
IJCV(54), No. 1-3, August 2003, pp. 183-209.
DOI Link
PDF File.
Software for Matlab: Code, Tracking.
WWW Link.
Earlier:
Learning Image Statistics for Bayesian Tracking,
ICCV01(II: 709-716).
IEEE DOI

de la Torre, F.[Fernando], Black, M.J.[Michael J.],
A Framework for Robust Subspace Learning,
IJCV(54), No. 1-3, August 2003, pp. 117-142.
DOI Link
PDF File.
Software for Matlab:
WWW Link. Code, PCA.

Wang, J.B.[Jing-Bo], Yan, S.[Sijie], Xiong, Y.J.[Yuan-Jun], Lin, D.[Dahua],
Motion Guided 3d Pose Estimation from Videos,
ECCV20(XIII:764-780).
Springer DOI
Code, Pose Estimation.
HTML Version.

HandVu Gesture Interface,
2010 Gesture recognition.
HTML Version. Code, Gesture. Gesture interface code.

Narasimhaswamy, S., Wei, Z., Wang, Y., Zhang, J., Nguyen, M.H.,
Contextual Attention for Hand Detection in the Wild,
ICCV19(9566-9575)
IEEE DOI
Code, Hand Detection.
WWW Link. convolutional neural nets, gesture recognition, object detection, unconstrained images, Hand-CNN, attention mechanism, Object detection

Shen, J.[Jie], Shi, W.Z.[Wen-Zhe], Pantic, M.[Maja],
HCI-lambda-2 Workbench: A development tool for multimodal human-computer interaction systems,
FG11(766-773).
IEEE DOI
Code, HCI.

Iashin, V.[Vladimir], Palermo, F.[Francesca], Solak, G.[Gökhan], Coppola, C.[Claudio],
Top-1 Corsmal Challenge 2020 Submission: Filling Mass Estimation Using Multi-modal Observations of Human-robot Handovers,
CORSMAL20(423-436).
Springer DOI
Code, HRI.
WWW Link. Human-robot object handover. the robot needs to estimate the filling mass of a container held by a human

Rathgeb, C.[Christian], Uhl, A.[Andreas], Wild, P.[Peter],
Iris Biometrics: From Segmentation to Template Security,
Springer2013. ISBN: 978-1-4614-5570-7

WWW Link.
Code, Iris Recognition. Includes software.

Othman, N.[Nadia], Dorizzi, B.[Bernadette], Garcia-Salicetti, S.[Sonia],
OSIRIS: An open source iris recognition software,
PRL(82, Part 2), No. 1, 2016, pp. 124-131.
Elsevier DOI
Code, Iris Recognition. Iris recognition

Ma, N.N.[Ning-Ning], Zhang, X.Y.[Xiang-Yu], Liu, M.[Ming], Sun, J.[Jian],
Activate or Not: Learning Customized Activation,
CVPR21(8028-8038)
IEEE DOI
WWW Link. Code, Training. Image segmentation, Codes, Semantics, Neurons, Switches, Object detection

Venkatesh, S., Rosin, P.L.,
Dynamic Threshold Determination by Local and Global Edge Evaluation,
GMIP(57), No. 2, March 1995, pp. 146-160.
Earlier: SPIE(1964), 1993, pp. 40-50. Code, Segmentation. The code is available on the vision list archive:
WWW Link.

Comaniciu, D.[Dorin], Meer, P.[Peter],
Robust Analysis of Feature Spaces: Color Image Segmentation,
CVPR97(750-755).
IEEE DOI
Code, Segmentation. Code, Segmentation, C++. For the C++ code:
HTML Version. Color quantization for segmentation. Map into another feature space.

Artola, A.[Aitor],
Semantic Segmentation: A Zoology of Deep Architectures,
IPOL(13), 2023, pp. 167-182.
DOI Link
Code, Semantic Segmentation.

Section, Multiple Entries: 8.3.4.3.4 Encoder-Decoder Networks for Semantic Segmentation Chapter Contents (Back)
Encoder-Decoder.
See also Neural Networks for Semantic Segmentation.
See also Semantic Segmentation, Label and Segment Together.

Zou, Y., Yu, Z., Liu, X., Kumar, B.V.K.V., Wang, J.,
Confidence Regularized Self-Training,
ICCV19(5981-5990)
IEEE DOI
Code, Segmentation.
WWW Link. image classification, image segmentation, iterative methods, unsupervised learning, pseudolabels, overconfident label belief, Semantics

Zhu, Z., Xu, M., Bai, S., Huang, T., Bai, X.,
Asymmetric Non-Local Neural Networks for Semantic Segmentation,
ICCV19(593-602)
IEEE DOI
Code, Segmentation.
WWW Link. image fusion, image segmentation, neural nets, asymmetric nonlocal neural networks, Semantics

Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.,
CCNet: Criss-Cross Attention for Semantic Segmentation,
ICCV19(603-612)
IEEE DOI
Code, Segmentation.
WWW Link. image segmentation, information retrieval, learning (artificial intelligence), recurrent neural nets, Complexity theory

Zhuang, J., Yang, J., Gu, L., Dvornek, N.,
ShelfNet for Fast Semantic Segmentation,
CVRSUAD19(847-856)
IEEE DOI
Code, Segmentation.
WWW Link. image segmentation, image understanding, semantic segmentation, PASCAL VOC dataset, PSPNet, ResNet34 backbone, ShelfNet, Realtime

Balarini, J.P.[Juan Pablo], Nesmachnow, S.[Sergio],
A C++ Implementation of Otsu's Image Segmentation Method,
IPOL(6), 2016, pp. 155-164.
DOI Link
Code, Segmentation. Code, Otsu Segmentation. Code, Segmentation, C++.
See also Threshold Selection Method from Grey-Level Histograms, A.

Cour, T., Yu, S., and Shi, J.,
Normalized cut image segmenation software,
Online2006.
WWW Link. Code, Segmentation. Code, Segmentation, C. Matlab Code for segmentation and clustering. C code for segmentation.
See also Normalized Cuts and Image Segmentation.

Xu, Y.H.[Yong-Hao], Ghamisi, P.[Pedram],
Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes With Point-Level Annotations,
IP(31), 2022, pp. 5038-5051.
IEEE DOI
Code, Segmentation.
WWW Link. Annotations, Image segmentation, Semantics, Training, Remote sensing, Knowledge transfer, Predictive models, Semantic segmentation, remote sensing

Dagobert, T.[Tristan], Grompone von Gioi, R.[Rafael], de Franchis, C.[Carlo], Morel, J.M.[Jean-Michel], Hessel, C.[Charles],
Cloud Detection by Luminance and Inter-band Parallax Analysis for Pushbroom Satellite Imagers,
IPOL(10), 2020, pp. 167-190.
DOI Link
Code, Cloud Detection. The nature of the pushbroom results in parallax for clouds relative to the ground.

Dagobert, T.[Tristan], Grompone von Gioi, R.[Rafael], Morel, J.M.[Jean-Michel], de Franchis, C.[Carlo],
Temporal Repetition Detector for Time Series of Spectrally Limited Satellite Imagers,
IPOL(10), 2020, pp. 62-77.
DOI Link
Code, Ground Visibility. Visibility in time series of satellite images. Not exactly cloud detection, but similar ideas.

von Gioi, R.G.[Rafael Grompone], Hessel, C.[Charles], Dagobert, T.[Tristan], Morel, J.M.[Jean-Michel], de Franchis, C.[Carlo],
Ground Visibility in Satellite Optical Time Series Based on A Contrario Local Image Matching,
IPOL(11), 2021, pp. 212-233.
DOI Link
Code, Ground Visibility. I.e. how much is cloud free.

Chabardès, T.[Théodore], Dokládal, P.[Petr], Faessel, M.[Matthieu], Bilodeau, M.[Michel],
A Parallel, O(n) Algorithm for an Unbiased, Thin Watershed,
IPOL(12), 2022, pp. 50-71.
DOI Link
Code, Watershed.
Earlier:
A parallel, O(N) algorithm for unbiased, thin watershed,
ICIP16(2569-2573)
IEEE DOI
Computer architecture

Cettour-Janet, P.[Pierre], Cazorla, C.[Clément], Machairas, V.[Vaia], Delannoy, Q.[Quentin], Bednarek, N.[Nathalie], Rousseau, F.[François], Décencière, E.[Etienne], Passat, N.[Nicolas],
Watervoxels,
IPOL(9), 2019, pp. 317-328.
DOI Link
Code, Segmentation. Voxels, derived from waterpixels which were drived from superpixels.
See also Waterpixels.

Gay, R.[Robin], Lecoutre, J.[Jérémie], Menouret, N.[Nicolas], Morillon, A.[Arthur], Monasse, P.[Pascal],
Bilateral K-Means for Superpixel Computation (the SLIC Method),
IPOL(12), 2022, pp. 72-91.
DOI Link
Code, Superpixel. Code, SLIC. SLIC: Simple linear iterative clustering.
See also SLIC Superpixels Compared to State-of-the-Art Superpixel Methods.

Uziel, R., Ronen, M., Freifeld, O.,
Bayesian Adaptive Superpixel Segmentation,
ICCV19(8469-8478)
IEEE DOI
Code, Segmentation.
WWW Link. Bayes methods, image colour analysis, image representation, image segmentation, mixture models, nonparametric statistics, Image color analysis

Liu, S.[Sheng], Liu, K.N.[Kang-Ning], Zhu, W.C.[Wei-Cheng], Shen, Y.Q.[Yi-Qiu], Fernandez-Granda, C.[Carlos],
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations,
CVPR22(2596-2606)
IEEE DOI
Code, Segmentation.
WWW Link. Training, Deep learning, Annotations, Shape, Semantics, Robustness, Pattern recognition, Segmentation, grouping and shape analysis

Xiong, H.P.[Hai-Peng], Lu, H.[Hao], Liu, C.X.[Cheng-Xin], Liu, L.[Liang], Shen, C.H.[Chun-Hua], Cao, Z.G.[Zhi-Guo],
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting,
IJCV(131), No. 7, July 2023, pp. 1722-1740.
Springer DOI

Earlier: A1, A2, A3, A4, A6, A5:
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer,
ICCV19(8361-8370)
IEEE DOI
Code, Counting.
WWW Link. divide and conquer methods, image processing, learning (artificial intelligence), neural nets, Estimation

Shi, Z.L.[Zeng-Lin], Mettes, P.S.[Pascal S.], Snoek, C.G.M.[Cees G. M.],
Counting With Focus for Free,
ICCV19(4199-4208)
IEEE DOI
Code, Counting.
WWW Link. convolutional neural nets, image segmentation, network theory (graphs), object detection, supervised learning, Convolution

Chen, Z., Yin, K., Fisher, M., Chaudhuri, S., Zhang, H.,
BAE-NET: Branched Autoencoder for Shape Co-Segmentation,
ICCV19(8489-8498)
IEEE DOI
Code, Convolutional Neural Networks.
WWW Link. convolutional neural nets, feature extraction, image reconstruction, image representation, image segmentation, Training

Guo, X.Q.[Xiao-Qing], Liu, J.[Jie], Yuan, Y.X.[Yi-Xuan],
Semantic-Oriented Labeled-to-Unlabeled Distribution Translation for Image Segmentation,
MedImg(41), No. 2, February 2022, pp. 434-445.
IEEE DOI
Code, Segmentation.
WWW Link. Image segmentation, Semantics, Feature extraction, Data models, Task analysis, Semisupervised learning, few sample segmentation

Bou, X.[Xavier],
A Study of RobustNet, a Domain Generalization Method for Semantic Segmentation,
IPOL(12), 2022, pp. 469-479.
DOI Link
Code, Domain Generalization. Code, Semantic Segmentation.
See also RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening.

Wang, X.L.[Xin-Long], Kong, T.[Tao], Shen, C.H.[Chun-Hua], Jiang, Y.N.[Yu-Ning], Li, L.[Lei],
SOLO: Segmenting Objects by Locations,
ECCV20(XVIII:649-665).
Springer DOI
Code, Segmentation.
WWW Link.

Ma, J.[Jin], Pang, S.M.[Shan-Min], Yang, B.[Bo], Zhu, J.H.[Ji-Hua], Li, Y.C.[Yao-Chen],
Spatial-Content Image Search in Complex Scenes,
WACV20(2492-2500)
IEEE DOI
Code, Image Search.
WWW Link. Visualization, Semantics, Image retrieval, Feature extraction, Image representation, Object detection

Fang, H., Sun, J., Wang, R., Gou, M., Li, Y., Lu, C.,
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting,
ICCV19(682-691)
IEEE DOI
Code, Segmentation.
WWW Link. convolutional neural nets, image annotation, image sampling, image segmentation, object detection, probability, Measurement

Sofiiuk, K., Sofiyuk, K., Barinova, O., Konushin, A., Barinova, O.,
AdaptIS: Adaptive Instance Selection Network,
ICCV19(7354-7362)
IEEE DOI
Code, Segmentation.
WWW Link. image segmentation, object detection, AdaIN layers, pixel-accurate object masks, semantic segmentation pipeline, Aerospace electronics

Liu, Y.F.[Yan-Feng], Psota, E.T.[Eric T.], Pérez, L.C.[Lance C.],
Layered Embeddings for Amodal Instance Segmentation,
ICIAR19(I:102-111).
Springer DOI
Code, Segmentation. Code available:
WWW Link.

Yu, Q.[Qian], Gao, Y.[Yang], Zheng, Y.F.[Ye-Feng], Zhu, J.B.[Jian-Bing], Dai, Y.K.[Ya-Kang], Shi, Y.H.[Ying-Huan],
Crossover-Net: Leveraging vertical-horizontal crossover relation for robust medical image segmentation,
PR(113), 2021, pp. 107756.
Elsevier DOI
Code, Segmentation.
WWW Link. Convolutional neural network, Non-elongated tissue, Crossover-Net, Image segmentation, Crossover-patch

Liu, Z.H.[Zi-Hao], Li, Z.W.[Zhuo-Wei], Hu, Z.Q.[Zhi-Qiang], Xia, Q.[Qing], Xiong, R.Q.[Rui-Qin], Zhang, S.T.[Shao-Ting], Jiang, T.T.[Ting-Ting],
Contrastive and Selective Hidden Embeddings for Medical Image Segmentation,
MedImg(41), No. 11, November 2022, pp. 3398-3410.
IEEE DOI
Code, Segmentation.
WWW Link. Uncertainty, Image segmentation, Training, Task analysis, Medical diagnostic imaging, Decoding, neural network

Yin, M.H.[Ming-Hao], Yao, Z.L.[Zhu-Liang], Cao, Y.[Yue], Li, X.[Xiu], Zhang, Z.[Zheng], Lin, S.[Stephen], Hu, H.[Han],
Disentangled Non-local Neural Networks,
ECCV20(XV:191-207).
Springer DOI
Code, Segmentation.
WWW Link.
WWW Link.

qsnake_demo,
2007. Code, Snakes.
HTML Version. Basic tool to play with snakes (active contour models).

Xu, C.Y.[Chen-Yang], Prince, J.L.[Jerry L.],
Generalized gradient vector flow external forces for active contours,
SP(71), No. 2, 15 December 1998, pp. 131-139.
Earlier:
Gradient Vector Flow: A New External Force for Snakes,
CVPR97(66-71).
IEEE DOI
Code, Snakes. Code:
HTML Version.

Arbelaez, P.[Pablo], Fowlkes, C.C.[Charless C.], and Martin, D.R.[David R.],
The Berkeley Segmentation Dataset and Benchmark,
Online2007. Dataset, Segmentation. Dataset, BSDS. Code, Segmentation.
WWW Link. The updated code and data for the earlier paper.
See also Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics, A.

Shor, R.[Ronen],
Intellegent Scissors: Interactive tool for image segmentation,
Online2008.
HTML Version. Code, Segmentation. Implementation of
See also Interactive Segmentation with Intelligent Scissors.

Naegel, B.[Benoît], Passat, N.[Nicolas],
Interactive Segmentation Based on Component-trees,
IPOL(2014), No. 2014, pp. 89-97.
DOI Link
Code, Segmentation.

Ashual, O., Wolf, L.,
Specifying Object Attributes and Relations in Interactive Scene Generation,
ICCV19(4560-4568)
IEEE DOI
Award, Marr Prize, HM. Code, Interactive Segmentation.
WWW Link. graph theory, interactive systems, object recognition, rendering (computer graphics), higher visual quality, Tools

Hou, Q.B.[Qi-Bin], Zhang, L.[Li], Cheng, M.M.[Ming-Ming], Feng, J.S.[Jia-Shi],
Strip Pooling: Rethinking Spatial Pooling for Scene Parsing,
CVPR20(4002-4011)
IEEE DOI
Code, Segmentation.
WWW Link. Strips, Kernel, Shape, Tensile stress, Semantics, Benchmark testing, Convolutional codes

Sumengen, B.[Baris], Manjunath, B.S.,
Graph Partitioning Active Contours (GPAC) for Image Segmentation,
PAMI(28), No. 4, April 2006, pp. 509-521.
IEEE DOI
Code, Segmentation. Code available
WWW Link.

Kuo, W., Angelova, A., Malik, J., Lin, T.,
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors,
ICCV19(9206-9215)
IEEE DOI
Code, Segmentation.
WWW Link. image segmentation, object detection, object recognition, pose estimation, shape recognition, supervised learning, Robots

map3d: Interactive scientific visualization tool for bioengineering data,
2006. Code, 3-D Visualization.
HTML Version. Scientific visualization application written to display and edit complex, three-dimensional geometric models and scalar, time-based data associated with those models.

Seg3D: Volumetric Image Segmentation and Visualization,
2006. Code, 3-D Segmentation.
HTML Version. Interactive segmentation tool that mixes powerful ITK and NRRD based volumetric image analysis and segmentation tools, interactive painting, and advanced volume rendering.

Santana-Cedrés, D.[Daniel], Monzón, N.[Nelson], Álvarez, L.[Luis],
An Algorithm for 3D Curve Smoothing,
IPOL(11), 2021, pp. 37-55.
DOI Link
Code, Curve Smoothing.

Boykov, Y.Y.[Yuri Y.], Kolmogorov, V.[Vladimir],
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision,
PAMI(26), No. 9, September 2004, pp. 1124-1137.
IEEE DOI

Earlier: EMMCVPR01(359-374).
Springer DOI
Code, Segmentation.
Earlier:
Computing geodesics and minimal surfaces via graph cuts,
ICCV03(26-33).
IEEE DOI
Combine geodesic active contours with graph cuts.
See also Exact Maximum a Posterori Estimation for Binary Images. Code is available:
WWW Link. Code, Energy Minimization.

Mishra, A.K.[Ajay K.], Aloimonos, Y.[Yiannis], Cheong, L.F.[Loong Fah],
Code: Active Segmentation With Fixation,
Online2010. Code, Segmentation. Code, Snakes.
HTML Version. Code for ICCV 2009 paper.
See also Active Segmentation with Fixation.

Alvarez, L.[Luis], Baumela, L.[Luis], Márquez-Neila, P.[Pablo], Henríquez, P.[Pedro],
A Real Time Morphological Snakes Algorithm,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
Code, Snakes.

Sumengen, B.[Baris],
Matlab toolbox for Level Set Methods,
Online2008.
HTML Version. Code, Segmentation. Code, Segmentation, Matlab. The code follows Osher and Fedkiw book.

Mitiche, A.[Amar], Ben Ayed, I.[Ismail],
Variational and Level Set Methods in Image Segmentation,
Springer2011, ISBN: 978-3-642-15351-8
WWW Link. Survey, Level Set Segmentation. Buy this book: Variational and Level Set Methods in Image Segmentation (Springer Topics in Signal Processing)
Code, Level Set Segmentation. Code:
WWW Link.

Fan, D.,
Level-set image segmenation software,
Online2002.
WWW Link. Code, Segmentation.

Pierre, F.[Fabien], Amendola, M.[Mathieu], Bigeard, C.[Clémence], Ruel, T.[Timothé], Villard, P.F.[Pierre-Frédéric],
Segmentation with Active Contours,
IPOL(11), 2021, pp. 120-141.
DOI Link
Code, Segmentation. Code, Active Contours. Free deformable model with fixedtopology. Level Set segmentation.

Ben Ayed, I.[Ismail], Mitiche, A.[Amar], Belhadj, Z.[Ziad],
Multiregion Level-Set Partitioning of Synthetic Aperture Radar Images,
PAMI(27), No. 5, May 2005, pp. 793-800.
IEEE Abstract.
SAR.
And:
Level Set Curve Evolution Partitioning of Polarimetric Images,
ICIP05(I: 281-284).
IEEE DOI
Code, Level Set Segmentation. Code:
WWW Link.

Briand, T.[Thibaud], Davy, A.[Axel],
Optimization of Image B-spline Interpolation for GPU Architectures,
IPOL(9), 2019, pp. 183-204.
DOI Link
Code, B-Spline. OpenCV code.

Buades, A.[Antoni], Le, T.M.[Triet M.], Morel, J.M.[Jean-Michel], Vese, L.A.[Luminita A.],
Cartoon+Texture Image Decomposition,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Texture.

Buades, A.[Antoni], Lisani, J.L.[Jose-Luis],
Directional Filters for Cartoon + Texture Image Decomposition,
IPOL(6), 2016, pp. 75-88.
DOI Link
Code, Image Decompostiong.

Felzenszwalb, P.F.[Pedro F.], Huttenlocher, D.P.[Daniel P.],
Efficient Graph-Based Image Segmentation,
IJCV(59), No. 2, September 2004, pp. 167-181.
DOI Link
PDF File. Code, Segmentation. And code:
WWW Link.

Edison: Edge Detection and Image SegmentatiON system,
Online2003.
HTML Version. Code, Segmentation. Code, Edge Detection.
See also Mean Shift: A Robust Approach Toward Feature Space Analysis.

Zhang, R., Tsai, P.S., Cryer, J.E., Shah, M.,
Shape from Shading: A Survey,
PAMI(21), No. 8, August 1999, pp. 690-706.
IEEE DOI Survey, Shape from Shading. Minimization:
See also Estimation of Illuminant Direction, Albedo, and Shape from Shading.
See also Shape from Shading with a Linear Triangular Element Surface Model. Propagation:
See also Simple Algorithm for Shape for Shading, A. Local:
See also Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System. Linear:
See also Shape Information from Shading: A Theory about Human Perception. (Would
See also On the Extraction of Shape Information from Shading. be better reference?)
See also Shape From Shading Using Linear-Approximation. (The text says 1992, but that is the conference reference) Code, Shape from Shading.
WWW Link.

Zhou, K.L.[Kai-Lai], Wang, Y.[Yibo], Lv, T.[Tao], Li, Y.Q.[Yun-Qian], Chen, L.[Linsen], Shen, Q.[Qiu], Cao, X.[Xun],
Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline,
CVPR22(3094-3105)
IEEE DOI
Code, Object Detection.
WWW Link. Shape, Image color analysis, Object detection, Predictive models, Pattern recognition, Task analysis, Synthetic aperture radar, Datasets and evaluation

Tsai, P.S.[Ping-Sing], Shah, M.[Mubarak],
Shape From Shading Using Linear-Approximation,
IVC(12), No. 8, October 1994, pp. 487-498.
Elsevier DOI Code, Shape from Shading.
HTML Version.
Earlier:
A Fast Linear Shape from Shading,
CVPR92(734-736).
IEEE DOI
And: Univ. of Central FloridaTR. Linear Approach. Short C program (25 lines) that converges in 2 interations.

Gu, J., Tu, C., Ramamoorthi, R., Belhumeur, P.N., Matusik, W., Nayar, S.K.,
Time-varying Surface Appearance: Acquisition, Modeling, and Rendering,
ToG(25), July 2006.
PDF File. Code, Surface Appearance.
WWW Link.

Lalonde, J.F.[Jean-Franšois], Efros, A.A.[Alexei A.], Narasimhan, S.G.[Srinivasa G.],
Estimating the Natural Illumination Conditions from a Single Outdoor Image,
IJCV(98), No. 2, June 2012, pp. 123-145.
WWW Link.

Earlier:
Estimating natural illumination from a single outdoor image,
ICCV09(183-190).
IEEE DOI
WWW Link.
Code, Illumination Estimation. Code:
WWW Link.

Kinect-Like 3D camera,
Online2012.
WWW Link. Code, Structured Light. Hacker (in the old sense of the word) description of how to build such a camera.

Lezama, J.[José], Randall, G.[Gregory], von Gioi, R.G.[Rafael Grompone],
Vanishing Point Detection in Urban Scenes Using Point Alignments,
IPOL(7), 2017, pp. 131-164.
DOI Link
Code, Vanishing Points.

Lezama, J.[José], Randall, G.[Gregory], Morel, J.M.[Jean-Michel], von Gioi, R.G.[Rafael Grompone],
An Unsupervised Point Alignment Detection Algorithm,
IPOL(5), 2015, pp. 296-310.
DOI Link
Code, Vanishing Points.

Meza, J.[Jhacson], Romero, L.A.[Lenny A.], Marrugo, A.G.[Andrés G.],
MarkerPose: Robust Real-time Planar Target Tracking for Accurate Stereo Pose Estimation,
LXCV21(1282-1290)
IEEE DOI
Code, Pose.
WWW Link. Circles for markers. Deep learning, Ultrasonic imaging, Target tracking, Pose estimation, Lighting

Recurrent Asynchronous Multimodal Networks + Events, Frames, Semantic labels, and Depth maps recorded in CARLA simulator,
2021
HTML Version. Code, Recurrent Networks. Code, Monocular Depth. Dataset, Monocular Depth.

Watanabe, M., Nayar, S.K.,
Rational Filters for Passive Depth from Defocus,
IJCV(27), No. 3, May 1998, pp. 203-225.
DOI Link
PDF File.
WWW Link.
Code, Depth from Focus.

Favaro, P.[Paolo], Soatto, S.[Stefano],
3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur,
Springer2007, ISBN 978-1-84628-176-1.
WWW Link. Code, Motion Blur. For implementations of relevant algorithms, test data and demos:
WWW Link.
See also Geometric Approach to Shape from Defocus, A.

Zhou, Y., Qi, H., Ma, Y.,
End-to-End Wireframe Parsing,
ICCV19(962-971)
IEEE DOI
Code, Wireframe.
WWW Link. computational geometry, feature extraction, image segmentation, object detection, end-to-end wireframe parsing, Training

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
Stereo Matching With Nonlinear Diffusion,
IJCV(28), No. 2, June-July 1998, pp. 155-174.
DOI Link

Earlier: CVPR96(343-350).
IEEE DOI
And: CornellComputer Science, TR96-1575, March 1996. Code, Stereo. Code:
HTML Version. Point matching using Sum of Squared Differences (SSD).

Darmon, F.[François], Monasse, P.[Pascal],
The Polar Epipolar Rectification,
IPOL(11), 2021, pp. 56-75.
DOI Link
Code, Stereo.
See also Epipolar geometry and log-polar transform in wide baseline stereo matching.

Lucas, B.D., and Kanade, T.,
An Iterative Image Registration Technique with an Application to Stereo Vision,
DARPA81(121-130).
HTML Version.
And: IJCAI81(674-679).
HTML Version. Code, Registration.
WWW Link. Another version in Matlab.
WWW Link. Uses differences in intensity between the two images and the local gradient of one image (both?) to compute the shift. A registration problem, but very applicable to stereo. For more generalization:
See also Shape and Motion from Image Streams: A Factorization Method Part 3 - Detection and Tracking of Point Features.

Baker, S.[Simon], Matthews, I.[Iain],
Lucas-Kanade 20 Years On: A Unifying Framework,
IJCV(56), No. 3, February-March 2004, pp. 221-255.
DOI Link

And:
Lucas-Kanade 20 Years On: A Unifying Framework: Part 1,
CMU-RI-TR-02-16, July 2002.
WWW Link.

And:
Lucas-Kanade 20 Years On,
CMU-RI2006, Project Description.
HTML Version. Code, Tracking. Matlab code is available.
See also Generalized Image Matching by the Method of Differences.
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.

Ogale, A.S.[Abhijit S.], Aloimonos, Y.[Yiannis],
Shape and the Stereo Correspondence Problem,
IJCV(65), No. 3, December 2005, pp. 147-162.
Springer DOI or:
PDF File.
Code, Stereo.
Earlier:
Robust contrast invariant stereo correspondence,
CRA05(xx-yy).
PDF File.
The influence of shape on image correspondence,
3DPVT04(945-952).
IEEE DOI

And:
Stereo Correspondence with Slanted Surfaces: Critical Implications of Horizontal Slant,
CVPR04(I: 568-573).
IEEE DOI Or:
PDF File.
Related code is also available:
HTML Version.

Hosni, A.[Asmaa], Rhemann, C.[Christoph], Bleyer, M.[Michael], Rother, C.[Carsten], Gelautz, M.[Margrit],
Fast Cost-Volume Filtering for Visual Correspondence and Beyond,
PAMI(35), No. 2, February 2013, pp. 504-511.
IEEE DOI

Earlier: A2, A1, A3, A4, A5: CVPR11(3017-3024).
IEEE DOI
Code, Stereo Matching. Matlab Code:
WWW Link. Code:
See also Stereo Disparity through Cost Aggregation with Guided Filter.

Julià, L.F.[Laura Fernández], Monasse, P.[Pascal],
Bilaterally Weighted Patches for Disparity Map Computation,
IPOL(5), 2015, pp. 73-89.
DOI Link
Code, Stereo Matching. Based on:
See also Distinctive Similarity Measure for stereo matching under point ambiguity.
See also Stereo Disparity through Cost Aggregation with Guided Filter.

Zhi, T., Pires, B.R., Hebert, M., Narasimhan, S.G.,
Deep Material-Aware Cross-Spectral Stereo Matching,
CVPR18(1916-1925)
IEEE DOI
WWW Link. Code, Stereo Matching. Glass, Cameras, Light sources, Clothing, Task analysis

Su, Q.[Qing], Ji, S.H.[Shi-Hao],
ChiTransformer: Towards Reliable Stereo from Cues,
CVPR22(1929-1939)
IEEE DOI
Code, Stereo.
WWW Link. Optical polarization, Biomedical optical imaging, Optical design, Stereo image processing, Estimation, Visual systems, Self- semi- meta- unsupervised learning

Chen, C., Chen, X., Cheng, H.,
On the Over-Smoothing Problem of CNN Based Disparity Estimation,
ICCV19(8996-9004)
IEEE DOI
Code, Stereo.
WWW Link. convolutional neural nets, estimation theory, image segmentation, learning (artificial intelligence), probability, Entropy

Scharstein, D.[Daniel], Szeliski, R.S.[Richard S.],
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms,
IJCV(47), No. 1-3, April-June 2002, pp. 7-42.
DOI Link
Code, Stereo. Dataset, Stereo. The data sets and code are also available:
WWW Link. Award, Everingham. for 2015

Marí, R.[Roger], Ehret, T.[Thibaud], Facciolo, G.[Gabriele],
Disparity Estimation Networks for Aerial and High-Resolution Satellite Images: A Review,
IPOL(12), 2022, pp. 501-526.
DOI Link
Code, Stereo.
WWW Link.
WWW Link. PSM and HSM learning architectures for disparity.

SRI Stereo Engine,
2007
WWW Link. Code, Stereo. Efficient implementation of area correlation stereo.

Real Time Dense Stereo,
2007
WWW Link. Code, Stereo. C++ library for real-time disparity estimation. It computes dense stereo matching from 2 or 3 images as well as 3D scene reconstruction.

Kolmogorov, V.[Vladimir], Zabih, R.[Ramin],
Multi-camera Scene Reconstruction via Graph Cuts,
ECCV02(III: 82 ff.). Award, ECCV.
Springer DOI

Earlier:
Computing Visual Correspondence with Occlusions via Graph Cuts,
ICCV01(II: 508-515).
IEEE DOI
Handle occlusions in correspondence. code is available:
WWW Link. Code, Stereo.

Chen, R.[Rui], Han, S.F.[Song-Fang], Xu, J.[Jing], Su, H.[Hao],
Visibility-Aware Point-Based Multi-View Stereo Network,
PAMI(43), No. 10, October 2021, pp. 3695-3708.
IEEE DOI

Earlier:
Point-Based Multi-View Stereo Network,
ICCV19(1538-1547)
IEEE DOI
Code, Stereo.
WWW Link. Image reconstruction, Geometry, Task analysis, Aggregates, 3D deep learning. computational geometry, image motion analysis, image texture, iterative methods, Feature extraction

Snavely, N.[Noah],
Bundler: Structure from Motion for Unordered Image Collections,
OnlineMay 2009. Code, Structure from Motion.
WWW Link.

Cryer, J.E.[James Edwin], Tsai, P.S.[Ping-Sing], Shah, M.[Mubarak],
Integration of Shape from Shading and Stereo,
PR(28), No. 7, July 1995, pp. 1033-1043.
Elsevier DOI Code, Shape from Shading.
HTML Version.
Earlier:
Integration of Shape from X Modules: Combining Stereo and Shading,
CVPR93(720-721).
IEEE DOI Related SfS analysis:
See also Analysis of Shape from Shading Techniques.

Getreuer, P.[Pascal],
A Survey of Gaussian Convolution Algorithms,
IPOL(2013), No. 2013, pp. 286-310.
DOI Link
Code, Gaussian Convolution. Used by Gabor, Canny (
See also Computational Approach to Edge Detection, A. ), and SIFT (
See also Distinctive Image Features from Scale-Invariant Keypoints. ) among other uses.

Demirovic, D.[Damir],
An Implementation of the Mean Shift Algorithm,
IPOL(9), 2019, pp. 251-268.
DOI Link
Code, Mean Shift.
See also review of mean-shift algorithms for clustering, A.
See also Mean Shift, Mode Seeking, And Clustering.

Gutierrez, J.A.[José A.], Armstrong, B.S.R.[Brian S.R.],
Precision Landmark Location for Machine Vision and Photogrammetry: Finding and Achieving the Maximum Possible Accuracy,
Springer2008, ISBN: 978-1-84628-912-5.
WWW Link. Code, Landmarks. Techniques to achieve optimal results. Buy this book: Precision Landmark Location for Machine Vision and Photogrammetry: Finding and Achieving the Maximum Possible Accuracy

Dai, X.Y.[Xi-Yang], Chen, Y.P.[Yin-Peng], Xiao, B.[Bin], Chen, D.D.[Dong-Dong], Liu, M.C.[Meng-Chen], Yuan, L.[Lu], Zhang, L.[Lei],
Dynamic Head: Unifying Object Detection Heads with Attentions,
CVPR21(7369-7378)
IEEE DOI
Code, Object Detection.
WWW Link. Location awareness, Codes, Computational modeling, Object detection, Detectors, Feature extraction

Wu, A.[Aming], Deng, C.[Cheng],
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation,
CVPR22(837-846)
IEEE DOI
Code, Object Detection.
WWW Link. Training, Representation learning, Visualization, Annotations, Object detection, Detectors, Performance gain, Transfer/low-shot/long-tail learning

Ge, Z.[Zheng], Liu, S.T.[Song-Tao], Li, Z.[Zeming], Yoshie, O.[Osamu], Sun, J.[Jian],
OTA: Optimal Transport Assignment for Object Detection,
CVPR21(303-312)
IEEE DOI
WWW Link. Code, Object Detection. Training, Costs, Codes, Transportation, Estimation, Object detection

Hou, Y.Z.[Yun-Zhong], Zheng, L.[Liang], Gould, S.[Stephen],
Multiview Detection with Feature Perspective Transformation,
ECCV20(VII:1-18).
Springer DOI
Code, Object Detection.
WWW Link. MultiviewX Dataset.

Yang, Z., Liu, S., Hu, H., Wang, L., Lin, S.,
RepPoints: Point Set Representation for Object Detection,
ICCV19(9656-9665)
IEEE DOI
Code, Object Detection.
WWW Link. object detection, object recognition, point set representation, object detection, modern object detectors, Training

Jiang, P., Hou, Q., Cao, Y., Cheng, M., Wei, Y., Xiong, H.,
Integral Object Mining via Online Attention Accumulation,
ICCV19(2070-2079)
IEEE DOI
Code, Object Detection.
WWW Link. image classification, image segmentation, object detection, object recognition, integral object mining, Benchmark testing

Wu, Z., Suresh, K., Narayanan, P., Xu, H., Kwon, H., Wang, Z.,
Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach,
ICCV19(1201-1210)
IEEE DOI
Code, Object Detection.
WWW Link. autonomous aerial vehicles, learning (artificial intelligence), object detection, transforms, free meta-data, UAV images, Detectors

Cieslewski, T.[Titus], Derpanis, K.G.[Konstantinos G.], Scaramuzza, D.[Davide],
SIPs: Succinct Interest Points from Unsupervised Inlierness Probability Learning,
3DV19(604-613)
IEEE DOI
PDF File.
Code, Interest Points.
WWW Link. Detectors, Measurement, Training, Feature extraction, Kernel, Interest points, detection

Cieslewski, T.[Titus], Bloesch, M.[Michael], Scaramuzza, D.[Davide],
Matching Features without Descriptors: Implicitly Matched Interest Points,
BMVC19(xx-yy).
PDF File. Code, Interest Points.
WWW Link.

Zhang, G.J.[Gong-Jie], Luo, Z.P.[Zhi-Peng], Yu, Y.C.[Ying-Chen], Cui, K.W.[Kai-Wen], Lu, S.J.[Shi-Jian],
Accelerating DETR Convergence via Semantic-Aligned Matching,
CVPR22(939-948)
IEEE DOI
Code, Detection Transformer.
WWW Link. Training, Costs, Semantics, Object detection, Transformers, Feature extraction, Recognition: detection, categorization, Motion and tracking

Halawani, A.[Alaa], Li, H.B.[Hai-Bo],
100 lines of code for shape-based object localization,
PR(60), No. 1, 2016, pp. 458-472.
Elsevier DOI
Code, Object Detection. Object detection

Roh, B.[Byungseok], Shin, W.[Wuhyun], Kim, I.[Ildoo], Kim, S.[Sungwoong],
Spatially Consistent Representation Learning,
CVPR21(1144-1153)
IEEE DOI
WWW Link. Code, Classification. Location awareness, Learning systems, Image segmentation, Codes, Object detection, Benchmark testing

Wolf, S.[Stefan], Meier, J.[Jonas], Sommer, L.[Lars], Beyerer, J.[Jürgen],
Double Head Predictor based Few-Shot Object Detection for Aerial Imagery,
LUAI21(721-731)
IEEE DOI
WWW Link. Code, Object Detection. Training, Head, Codes, Annotations, Training data

Siam, M., Oreshkin, B., Jagersand, M.,
AMP: Adaptive Masked Proxies for Few-Shot Segmentation,
ICCV19(5248-5257)
IEEE DOI
Code, Segmentation.
WWW Link. image fusion, image motion analysis, image segmentation, learning (artificial intelligence), AMP, adaptive masked proxies, Feature extraction

Swin-Transformer-Object-Detection,
Online2021.
WWW Link. Code, Swin Transform.

Choi, M.K.[Min-Kook], Jung, H.[Heechul],
Development of Fast Refinement Detectors on AI Edge Platforms,
IML20(592-606).
Springer DOI
Code, Object Detection.
WWW Link. Object detection on GPU

Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.,
CenterNet: Keypoint Triplets for Object Detection,
ICCV19(6568-6577)
IEEE DOI
Code, Object Detection.
WWW Link. neural nets, object detection, MS-COCO dataset, representative one-stage keypoint-based detector, CenterNet, Task analysis

Hong, X.P.[Xiao-Peng], Xu, Y.Y.[Ying-Yue], Zhao, G.Y.[Guo-Ying],
LBP-TOP: A Tensor Unfolding Revisit,
SFBA16(I: 513-527).
Springer DOI
Local Binary Pattern histograms from Three Orthogonal Planes. Code LPB-TOP.
WWW Link.

Neves, A.J.R.[António J. R.], Trifan, A.[Alina], Cunha, B.[Bernardo],
UAVision: A Modular Time-Constrained Vision Library for Color-Coded Object Detection,
CompIMAGE14(351-362).
Springer DOI
Code, Object Detection. Apply to soccer, traffic signs.

Oyallon, E.[Edouard], Rabin, J.[Julien],
An Analysis of the SURF Method,
IPOL(5), 2015, pp. 176-218.
DOI Link
Code, SURF. General section:
See also Scale Invariant Features, SIFT, SURF, ASIFT.

Zhao, J., Liu, J., Fan, D., Cao, Y., Yang, J., Cheng, M.,
EGNet: Edge Guidance Network for Salient Object Detection,
ICCV19(8778-8787)
IEEE DOI
Code, Object Detection.
WWW Link. convolutional neural nets, edge detection, feature extraction, image fusion, object detection, edge guidance network, Fuses

Wu, Z., Su, L., Huang, Q.,
Stacked Cross Refinement Network for Edge-Aware Salient Object Detection,
ICCV19(7263-7272)
IEEE DOI
Code, Object Detection.
WWW Link. convolutional neural nets, edge detection, image enhancement, image segmentation, Computational modeling

Mishkin, D.[Dmytro], Radenovic, F.[Filip], Matas, J.G.[Jiri G.],
Repeatability Is Not Enough: Learning Affine Regions via Discriminability,
ECCV18(IX: 287-304).
Springer DOI
Code, Affine Shape.
WWW Link.

Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., Fu, K.,
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects,
ICCV19(8231-8240)
IEEE DOI
Code, Object Detection.
WWW Link. feature extraction, image fusion, object detection, SCRDet, robust detection, natural images, Semantics

Franco, J.S., Boyer, E.,
Exact polyhedral visual hulls,
BMVC03(xx-yy).
HTML Version.
Code, Convex Hull.
WWW Link.

Normand, N.[Nicolas], Strand, R.[Robin], Evenou, P.[Pierre], Arlicot, A.[Aurore],
A Streaming Distance Transform Algorithm for Neighborhood-Sequence Distances,
IPOL(2014), No. 2014, pp. 196-203.
DOI Link
Code, Distance Transform.

Felzenszwalb, P.F.[Pedro F.], Huttenlocher, D.P.[Daniel P.],
Distance Transforms of Sampled Functions,
Cornell2004, Computing and Information Science TR2004-1963. Code, Distance Transform.
WWW Link.

Fedorov, V.[Vadim], Ballester, C.[Coloma],
An Affine Invariant Patch Similarity,
IPOL(8), 2018, pp. 490-513.
DOI Link
Code, Region Matching.

Dalitz, C.[Christoph], Wilberg, J.[Jens], Jeltsch, M.[Manuel],
The Gradient Product Transform: An Image Filter for Symmetry Detection,
IPOL(9), 2019, pp. 413-431.
DOI Link
Code, Symmetry. Apply to blood vessels and rotational symmetries.
See also Detection of symmetry points in images.

Li, Y.L.[Yong-Lu], Xu, Y.[Yue], Mao, X.H.[Xiao-Han], Lu, C.[Cewu],
Symmetry and Group in Attribute-Object Compositions,
CVPR20(11313-11322)
IEEE DOI
Code, Learning.
WWW Link. Couplings, Task analysis, Visualization, Pattern recognition, Training, Linguistics

Coeurjolly, D.[David], Kerautret, B.[Bertrand], Lachaud, J.O.[Jacques-Olivier],
Extraction of Connected Region Boundary in Multidimensional Images,
IPOL(2014), No. 2014, pp. 30-43.
DOI Link
Code, Connected Components.

Rengers, N.[Norman], Prinz, T.[Torsten],
JAVA-based Texture Analysis Employing Neighborhood Gray-Tone Difference Matrix (NGTDM) for Optimization of Land Use Classifications in High Resolution Remote Sensing Data,
PFG(2009), No. 5, 2009, pp. 455-467.
WWW Link.
Code, Texture Analysis. Code, Texture Analysis, Java.

Galerne, B.[Bruno], Gousseau, Y.[Yann], Morel, J.M.[Jean-Michel],
Micro-Texture Synthesis by Phase Randomization,
IPOL(2011), No. 1, 2011, pp. xx-yy.
DOI Link
Code, Texture Synthesis.

Total found: 787

For more information on the topics, contact information, etc. see the annotated Computer Vision Bibliography or the Complete Conference Listing for Computer Vision and Image Analysis

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