Computer Vision Systems, by NameVarious names have been used for computer vision systems. This lists some of the more famous systems. You may want to see the Source Code Listing or the the Vendor Listing for implementations or companies that will provide a finished product. Systems Referenced by NameFor 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 SystemSection, Multiple Entries: 15.3.1.4 Carnegie Mellon NAVLAB, AMBLER, etc. Chapter Contents (Back)Autonomous Vehicle. Vehicle Control. System: NAVLAB. Road Following. Path Planning.
Thorpe, C.E., (ed.), Vision and Navigation, the Carnegie Mellon NAVLAB, Norwell MA: Kluwer1990, 384 pages. Indexed by: BibRef 9000 NAVLAB90 System: NAVLAB. The BibRef Bookdescription of the NAVLAB project. Many of the following reports become redundant. BibRef
Bares, J., Hebert, M., Kanade, T., Krotkov, E., Mitchell, T., Simmons, R., and Whittaker, W., AMBLER: An Autonomous Rover for Planetary Exploration, Computer(22), No. 6, June 1990, pp. 18-26. System: AMBLER. The description of the whole project, not much vision. BibRef 9006
Shafer, S.A., and Whittaker, W., Development of an Integrated Mobile Robot System at Carnegie Mellon University: December 1989 Final Report, CMU-RI-TR-90-12, January 1990. BibRef 9001 Earlier: June 1987 Annual Report: Development of an Integrated Mobile Robot System at Carnegie Mellon, CMU-RI-TR-88-10, July 1987. System: Codger. System: NGS. The report on the NAVLAB project and its pieces. BibRef
Section, Multiple Entries: 15.3.3.3 CMU Road Followers -- ALVINN YARF MANIAC Chapter Contents (Back) Road Following. Path Planning. YARF. ALVINN. System: ALVINN.
Pomerleau, D.A., Neural Network Perception for Mobile Robot Guidance, Hingham: KluwerAcademic, 1993, 208 pp. ISBN 0-7923-9373-2. WWW Version. BibRef 9300 And: Ph.D.Thesis (CS), February 1992, BibRef CMU-CS-TR-92-115. System: ALVINN. The Neural-Network road follower (ALVINN). Has run on real highways with light traffic at 55mph. BibRef
Jochem, T.M., Pomerleau, D.A., and Thorpe, C.E., MANIAC: A Next Generation Neurally Based Autonomous Road Follower, DARPA93(473-479). BibRef 9300 And: A1 only: IAS93(xx-yy). System: MANIAC. BibRef
Pomerleau, D.A., Gowdy, J., and Thorpe, C.E., Combining Artificial Neural Networks and Symbolic Processing for Autonomous Robot Guidance, DARPA92(961-967). Neural Networks. System: YARF. YARF plus neural net for road tracking. ALVINN. BibRef 9200
Sukthankar, R.[Rahul], Pomerleau, D.A., and Thorpe, C.E., Panacea: An Active Sensor Controller for the ALVINN Autonomous Driving System, CMU-RI-TR-93-09, April 1993. System: ALVINN. Adds steering of the camera to ALVINN, this improves performance where sharp turns are required. BibRef 9304
Lawton, D.T., and McConnell, C.C., Image Understanding Environments, PIEEE(76), No. 8, August 1988, pp. 1036-1050. Survey, Systems. Systems, Survey. General discussion of IU Environments in terms of components, representations, programming constructs, data bases, and interfaces with examples (mostly from ADS systems). BibRef 8808
Konstantinides, K., Rasure, J.R., The Khoros Software Development Environment for Image and Signal Processing, IP(3), No. 3, May 1994, pp. 243-252. WWW Version. System: Khoros. Khoros. General overview of the Khoros system, image processing and visual programming environment. An online tutorial is available: WWW Version. BibRef 9405
Williams, T.D., Image Understanding Tools, ICPR90(II: 606-610). WWW Version. System: KBVision. The KBVision Environment. BibRef 9000
Hamey, L.G.C., Webb, J.A., and Wu, I.C., An Architecture Independent Programming Language for Low-Level Vision, CVGIP(48), No. 2, November 1989, pp. 246-264. WWW Version. System: APPLY. The apply system originally developed for the Warp. BibRef 8911
Tamura, H., Sakane, S., Tomita, F., Yokoya, N., Kaneko, M., Sakaue, K., Design and Implementation of SPIDER: A Transportable Image Processing Software Package, CVGIP(23), No. 3, September 1983, pp. 273-294. WWW Version. System: Spider. BibRef 8309 Earlier: A1, A3, A2, A4, A6, A5: A Transportable Image Processing Software System: SPIDER, ICPR82(75-78). BibRef
Haralick, R.M., Gipsy: General Image Processing System, TRIntelligent Systems Laboratory, University Of Washington. System: Gipsy. BibRef 0000
Groningen Image Processing System, GIPSY, TR1992. WWW Version. System: Gipsy. Code, Image Processing. BibRef 9200
Pope, A.R., Lowe, D.G., Vista: A Software Environment for Computer Vision Research, CVPR94(768-772). IEEE Abstract. IEEE Top Reference. System: Vista. Code, Image Analysis. HTML Version. BibRef 9400
Haralick, R.M.[Robert M.], Currier, P.[Phil], Image Discrimination Enhancement Combination System (IDECS), CGIP(6), No. 4, August 1977, pp. 371-381. WWW Version. System: IDECS. Describes their hardware system. Operates at video rates, TV size, data disk storage, input/output connected by a switch, with digital and analog processors. BibRef 7708
Section, Multiple Entries: 19.2.11 Hardware -- Image Understanding Architecture, IUA Chapter Contents (Back) Parallel Systems. IUA. System: IUA.
Weems, C.C., Levitan, S.P., Hanson, A.R., and Riseman, E.M., The Image Understanding Architecture, IJCV(2), No. 3, January 1989, pp. 251-282. WWW Version. BibRef 8901 Earlier: DARPA87(483-496). BibRef And: COINS-TR-87-76, August 1987. System: IUA. More on the design of the multi level system to go on top of CAAP, a 512X512 SIMD array, with a 64X64 array of 16 bit processors, with a 8X8 array of MIMD Lisp machines on top. BibRef
Weems, C.C., Rana, D., Hanson, A.R., Riseman, E.M., Shu, D.B., and Nash, J.G., An Overview of Architecture Research for Image Understanding at the University of Massachusetts, ICPR90(II: 379-384). WWW Version. System: IUA. BibRef 9000
Matsuyama, T., and Hwang, V., SIGMA: A Knowledge-Based Aerial Image Understanding System, New York: Plenum1990, 296 pp. ISBN 0-036-43301-X. System: Sigma. The BibRef 9000 Bookon SIGMA. BibRef
Hwang, V., Davis, L.S., and Matsuyama, T., Hypothesis Integration in Image Understanding Systems, CVGIP(36), No. 2/3, November/December 1986, pp. 321-371. WWW Version. BibRef 8611 Earlier: A1, A3 only: SIGMA: A Framework for Image Understanding: Integration of Bottom-Up and Top-Down Analyses, IJCAI85(908-915) BibRef And: The Sigma Image Understanding System, CVWS85(17-26). System: SIGMA. Application, Cartography. Find regions and structures, guided by a detailed model of what is there and how it appears. BibRef
Section, Multiple Entries: 22.1.3 GIS: Systems Implementation and Design Chapter Contents (Back) Systems. GIS.
McGlone, J.C., and Shufelt, J.A., Incorporating Vanishing Point Geometry into a Building Extraction System, DARPA93(437-448). BibRef 9300 And: Incorporating Vanishing-Point Geometry in Building Extraction Techniques, SPIE(1944), 1993, pp. 273-284. System: BABE. Verification of predicted buildings. BibRef
Kuan, D.T.[Darwin T.], and Drazovich, R.J.[Robert J.], Model Based Interpretation Of 3-D Range Data, T3DMP86(219-230). BibRef 8600 Earlier: Model-Based Interpretation of Range Imagery, AAAI-83(210-215). System: ACRONYM. Generalized cylinder models, laser range input, uses the ACRONYM approach, but applied to range data. BibRef
ITT Visual Information Solutions, Image Processing and Analysis. WWW Version. Vendor, Image Analysis. System: ENVI. Developed from early NASA work on Mariner.
Mulder, J.A., Mackworth, A.K., and Havens, W.S., Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach, PAMI(10), No. 6, November 1988, pp. 866-879. IEEE Abstract. IEEE Top Reference. WWW Version. System: Mapsee. This paper discusses Mapsee-1, -2, and -3 and thus serves as the primary reference for information about them. The conclusion is that schema-based representations with hierarchical (arc) consistency is best for a structured approach to visual knowledge. This set of systems illustrates the power of a schema based representation and a hierarchical constraint satisfaction algorithm. All three use a general segmentation of the image into regions and lines segments. Constraints are given to each feature based directly on its appearance. Mapsee-1 was a basic implementation of constraint satisfaction (arc-consistency) with no hierarchy in the representation and weak representations of constraints. Mapsee-2 added schemata as a means to improve the descriptive capabilities with hierarchical descriptions of the objects. This leads to a hierarchical arc consistency algorithm. Mapsee-3 provided a uniform representation for objects and relations between them (as schemata) and a more powerful representation of alternatives in the arc consistency algorithm. See also Discrimination Vision. See also Consistency in a Network of Relations. BibRef 8811
Reiter, R.[Raymond], and Mackworth, A.K.[Alan K.], A Logical Framework for Depiction and Image Interpretation, AI(41), No. 2, December 1989, pp. 125-156. WWW Version. BibRef 8912 And: The Logic of Depiction, RBCV-TR-87-18, June 1987, Toronto. System: Mapsee. This proposes a theory to formalize domain knowledge and is illustrated by specifying some general examples. Intended to provide a framework to analyze Mapsee and understand constraint satisfaction techniques. See also Consistency in a Network of Relations. BibRef
Nayar, S.K.[Shree K.], Nene, S.A.[Sameer A.], Murase, H.[Hiroshi], Subspace Methods for Robot Vision, RA(12), No. 5, October 1996, pp. 750-758. 9610 BibRef Earlier: A3, A1, A2: General Learning Algorithm for Robot Vision, ARPA94(I:753-763). System: SLAM. Software Library for Appearance Matching BibRef
Barrow, H.G., and Tenenbaum, J.M., MSYS: A System for Reasoning about Scenes, SRI AICenterTN 108, 1975. BibRef 7500 And: SRI AIMemo121, April 1976. Knowledge-Based Vision. System: MSYS. The MSYS Report. Use inexact reasoning on uncertain data to interpret regions extracted from an image. MSYS is an asynchronous relaxation process that applies the rules imposed by the modeluntil the labels are consistent. Constraints such as surface height and orientation can bu used. Relations between objects in the scene (hence regions in the image) can be used.. An M* (modified A*) search is used. For application in IGS: See also Experiments in Interpretation Guided Segmentation. BibRef
Section, Multiple Entries: 13.6.2 ACRONYM and SUCCESSOR Papers - Stanford University and Others Chapter Contents (Back) Recognition, Model Based. Model Based Recognition. Object Recognition. Matching, Models. ACRONYM. Model Based Recognition. System: ACRONYM. System: Successor.
Pichumani, R.[Ramani], CVonline: Model-based vision, CV-Online1997. HTML Version. System: Successor. A summary of the Successor system. BibRef 9700
Section, Multiple Entries: 13.6.3 University of Massachusetts VISIONS System Chapter Contents (Back) Recognition, Model Based. Model Based Recognition. Object Recognition. Matching, Models. VISIONS. Model Based Recognition. System: VISIONS. See also Complete Systems Derived from the Univ. Massachusetts Work.
Hanson, A.R., and Riseman, E.M., VISIONS: A computer System for Interpreting Scenes, CVS78(303-333). Multiple Resolutions. System: VISIONS. The basic outline of their system. For the full set of papers and a more complete description: See also University of Massachusetts VISIONS System. BibRef 7800
Wesley, L.P., and Hanson, A.R., The Use of an Evidential Based Model for Representing Knowledge and Reasoning about Images in the VISIONS System, CVWS82(14-25). BibRef 8200 And: COINSTR 82-29, December 1982. System: VISIONS. Outlines some of the ideas behind Shafer and Dempster to combine evidence. Basically evidence is a pair [support, plausibility], minimum and maximum amount that the evidence confirms the proposition. See also Mathematical Theory of Evidence, A. BibRef
Mundy, J.L., and Joynson, R., Constraint-Based Modeling, DARPA89(425-442). System: GEOMETER. Combining the GEOMETER system with reasoning for recognition. BibRef 8900
Strat, T.M., and Fischler, M.A., Context-Based Vision: Recognizing Objects Using Information from Both 2-D and 3-D Imagery, PAMI(13), No. 10, October 1991, pp. 1050-1065. IEEE Abstract. IEEE Top Reference. WWW Version. System: Condor. BibRef 9110 Earlier: A Context-Based Recognition System for Natural Scenes and Complex Domains, DARPA90(456-472). BibRef Earlier: A2, A1: Recognizing Objects in a Natural Environment: A Contextual Vision System, DARPA89(774-796). BibRef And: Context-Based Vision: Recognition of Natural Scenes, Asilomar89(532-536). System: CVS. Recognition, Context Based. This discusses the current SRI high-level vision effort. Addresses: object recognition without accurate object delineation, use of contest, use of geometry, and control of complexity. Uses context sets and cliques. BibRef
Strat, T.M., Natural Object Recognition, New York: Springer1992, 165pp. ISBN 0-387-97832-1. BibRef 9200 And: STAN-CS-91-1376, Stanford, CA, December 1990. BibRef Ph.D.Thesis. System: Condor. Rule Based Analysis. The BibRef Bookfrom his thesis on general object recognition using contextual cues. A set of processes interact through shared data structures. Each process has an associated context set, that when satisfied causes the process to run. BibRef
Strat, T.M., Using Context to Control Computer Vision Algorithms, Ascona95(3-12). BibRef 9500 Earlier: Employing Contextual Information in Computer Vision, DARPA93(217-229). System: Condor. The use of context in understanding objects. Describes the Prolog-like language used to control algorithms in RCDE BibRef
Shafer, S.A., and Kanade, T., Recursive Region Segmentation by Analysis of Histograms, ICASSP82(1166-1171). Segmentation, Systems. Phoenix. System: Phoenix. HTML Version. See also Phoenix Image Segmentation System: Description and Evaluation, The. After implementing a version of the Ohlander segmentation technique, Shafer proposed and implemented a variation that used the type of regions generated by the various possible threshold to determine the optimal threshold. This method applied all reasonable thresholds, as determined by analyzing the histograms, and chose the set of regions which were the most compact and had the clearest borders. This is based on the observation that, often, several histograms have peaks that correspond to the same regions, but one may give a more precise split than another even when its peak is not as clear according to the given criteria. BibRef 8200
Laws, K.I., The Phoenix Image Segmentation System: Description and Evaluation, SRI AICenter-TN 289, December 1982. Evaluation, Segmentation. System: Phoenix. Phoenix. Segmentation, Evaluation. BibRef 8212
Section, Multiple Entries: 8.3.2 Complete Systems Derived from the Univ. Massachusetts Work Chapter Contents (Back) Segmentation, Histogram. System: VISIONS. See also University of Massachusetts VISIONS System.
Hanson, A.R., and Riseman, E.M., Segmentation of Natural Scenes, CVS78(xx-yy). System: VISIONS. BibRef 7800
Matsuyama, T., Expert Systems for Image Processing: Knowledge-Based Composition of Image Analysis Processes, CVGIP(48), No. 1, October 1989, pp. 22-49. WWW Version. BibRef 8910 Earlier: ICPR88(I: 125-133). WWW Version. 8811 Rule Based Systems. System: SIGMA. This builds on the general systems such as SIGMA and is directed toward segmentation. BibRef
Tenenbaum, J.M., and Barrow, H.G., Experiments in Interpretation Guided Segmentation, AI(8), No. 3, June 1977, pp. 241-274. WWW Version. BibRef 7706 And: SRI AICenter-TN 123, March 1976. BibRef And: IGS: A Paradigm for Integrating Image Segmentation and Interpretation, PRAI-76(472-507). BibRef And: ICPR76(504-513). BibRef And: CMetImAly77(435-444). Segmentation, Knowledge. System: IGS. The key idea is that image elements can be reliably clustered into regions if semantic interpretations are used in addition to the raw image values. This builds on the interpretation ideas of MSYS ( See also MSYS: A System for Reasoning about Scenes. ). Unlike the work in Yakimovsky and Feldman, the relations between different types of regions are either possible or impossible. Initial interpretations are based on the image data, but extra interpretations at this point are not harmful. An iterative procedure is used to eliminate interpretations that are not valid given all the possible interpretations of the neighbors. When adjacent regions have the same interpretation they can be merged. This method requires a very specific model of the possible scene to provide any benefit. BibRef
Guzman-Arenas, A., Computer Recognition of Three-Dimensional Objects in a Visual Scene, MIT Project MAC-TR-59, December 1968, BibRef 6812 Ph.D.Thesis (EE). BibRef And: MIT AI-TR228. WWW Version. System: SEE. Use the junction labels and group the polyhedral scenes into separate bodies. NOT restricted to tri-hedral angles. BibRef
Mackworth, A.K., Interpreting Pictures of Polyhedral Scenes, AI(4), No. 2, June 1973, pp. 121-139. WWW Version. BibRef 7306 Earlier: IJCAI73(557-563). System: Poly. Introduce the dual space concept for interpreting scenes. BibRef
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