4.8.2 Perceptual Grouping, General Systems

Kelly, R.E., McConnell, P.R.M., Mildenberger, S.J.,
The Gestalt Photomapping System,
PhEngRS(43), No. 11, November 1977, pp. 1407-1417. BibRef 7711

Smith, B.J.,
Perception of Organization in a Random Stimulus,
CVGIP(31), No. 2, August 1985, pp. 242-247.
WWW Version. BibRef 8508
Earlier: ICPR84(512-514). BibRef

Lowe, D.G.,
Perceptual Organization and Visual Recognition,
Boston: KluwerAcademic Publishers, June 1985. BibRef 8506 Ph.D.Thesis (CS). ISBN 0-89838-172-X. Grouping, Perceptual. Grouping, Models. Recognition, Model Based. See also Recovery of Three-Dimensional Structure from Image Curves, The.
WWW Version. BibRef

Lowe, D.G.[David G.], and Binford, T.O.[Thomas O.],
The Perceptual Organization of Visual Images: Segmentation as a Basis for Recognition,
DARPA83(203-209). BibRef 8300
And:
Perceptual Organization as a Basis for Visual Recognition,
AAAI-83(255-260). BibRef
Earlier:
Segmentation and Aggregation: An Approach to Figure-Ground Phenomena,
DARPA82(168-178), BibRef RCV87(282-292). Figure-Ground separation. Bottom up grouping is a prerequisite for recognition. This breaks into 3 types of grouping: segmentation, 3D interpretation, descriptions of objects. BibRef

Lowe, D.G.,
Integrated Treatment of Matching and Measurement Errors for Robust Model-Based Motion Tracking,
ICCV90(436-440).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9000

Sankar, P.V., and Sharma, C.U., Narasimhan, R.,
Computing the Organizations and Shapes of Two-Dimensional Dot Patterns: A Perceptual-Level Approach,
CGIP(8), No. 2, October 1978, pp. 203-218.
WWW Version. BibRef 7810

Dickson, W.,
Feature Grouping in a Hierarchical Probabilistic Network,
IVC(9), No. 1, February 1991, pp. 51-57.
WWW Version. BibRef 9102
Earlier: BMVC90(xx-yy).
PDF Version. 9009 BibRef

Mohan, R., and Nevatia, R.,
Perceptual Organization for Scene Segmentation and Description,
PAMI(14), No. 6, June 1992, pp. 616-635.
IEEE Abstract. IEEE Top Reference.
WWW Version. BibRef 9206 USC Computer Vision BibRef
Earlier:
Segmentation and Description Based on Perceptual Organization,
CVPR89(333-341).
IEEE Abstract. IEEE Top Reference. BibRef
And:
Perceptual Organization for Segmentation and Description,
DARPA89(415-424). Segmentation, Grouping. Groupings of line features are located by co-curvilinearity and symmetry to find curves, symmetries and ribbons. These combine to give 2-D shapes and object surfaces. Combination uses a Hopfield network. See also Using Perceptual Organization to Extract 3-D Structures. BibRef

Mohan, R.,
Perceptual Organization for Computer Vision,
USC_IRISTR-254, August 1989, BibRef 8908 Ph.D.Thesis (CS). Thesis with perceptual organization for segmentation and matching applications. BibRef

Ahuja, N., and Tuceryan, M.,
Extraction of Early Perceptual Structure in Dot Patterns: Integrating Region, Boundary, and Component Gestalt,
CVGIP(48), No. 3, December 1989, pp. 304-356.
WWW Version. BibRef 8912
Earlier: A2, A1:
Perceptual Segmentation of Nonhomogeneous Dot Patterns,
CVPR83(47-52). Relaxation. Group dots into the perceptual groups using multiple constraints. BibRef

Tuceryan, M., Jain, A.K., Ahuja, N.,
Supervised classification of early perceptual structure in dot patterns,
ICPR92(II:88-91).
IEEE DOI may work or IEEE-CS DOI may work. 9208 BibRef

Mjolsness, E., Gindi, G., and Anandan, P.,
Optimization in Model Matching and Perceptual Organization,
NeurComp(1), 1989, pp. 218-229. BibRef 8900
And:
Optimization in Model Matching and Perceptual Organization: A First Look,
YaleCS, YaleU/DCS/RR-634, June 1988. Hopfield network. BibRef

Sarkar, S., Boyer, K.L.,
Perceptual Organization in Computer Vision: A Review and a Proposal for a Classificatory Structure,
SMC(23), No. 2, 1993, pp. 382-399. BibRef 9300

Sarkar, S., and Boyer, K.L.,
Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization,
PAMI(15), No. 3, March 1993, pp. 256-274.
IEEE Abstract. IEEE Top Reference.
WWW Version. Bayes Nets. BibRef 9303
Earlier:
Perceptual Organization Using Bayesian Networks,
CVPR92(251-256).
IEEE Abstract. IEEE Top Reference. Integrate a number of different systems. BibRef

Sarkar, S., Boyer, K.L.,
Using Perceptual Inference Networks To Manage Vision Processes,
CVIU(62), No. 1, July 1995, pp. 27-46.
WWW Version. BibRef 9507
Earlier: ICPR94(A:808-810).
IEEE DOI may work or IEEE-CS DOI may work. BibRef

Sarkar, S., and Boyer, K.L.,
Computing Perceptual Organization in Computer Vision,
World Scientific1994. (ISBN: 981-02-1832-X). 232pp. BibRef 9400 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. BibRef

Sarkar, S., Boyer, K.L.,
Automated Design of Bayesian Perceptual Inference Networks,
CVPR94(98-103).
IEEE Abstract. IEEE Top Reference. BibRef 9400

Sarkar, S., Boyer, K.L.,
A Computational Structure for Preattentive Perceptual Organization: Graphical Enumeration and Voting Methods,
SMC(24), 1994, pp. 246-267. BibRef 9400

Sarkar, S., Boyer, K.L.,
Computing Perceptual Organization Using Voting Methods and Graphical Enumeration,
ICPR92(I:263-267).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9200

Pun, T.[Thierry],
Electromagnetic Models for Perceptual Grouping,
AMV Strategies921992, pp. 129-149. BibRef 9200

Saund, E.[Eric],
Putting Knowledge into a Visual Shape Representation,
AI(54), No. 1-2, March 1992, pp. 71-119.
WWW Version. BibRef 9203
And:
The Role of Knowledge in Visual Shape Representation,
MIT AI-TR-1092, October 1988.
WWW Version. BibRef

Chen, L.H.,
A New Approach for Feature Point Classification, Aggregation, and Description,
PRAI(6), 1992, pp. 849-871. BibRef 9200

von der Malsburg, C.[Christoph], and Schneider, W.,
A Neural Cocktail-Party Processor,
BioCyber(54), 1986, pp. 29-40. BibRef 8600

von der Malsburg, C.[Christoph], and Buhmann, J.,
Sensory Segmentation with Coupled Neural Oscillators,
BioCyber(67), 1993, pp. 233-242. BibRef 9300

Sompolinsky, H., Golomb, D., and Kleinfeld, D.,
Global Processing of Visual Stimuli in a Neural Network of Coupled Oscillators,
NAS(87), September 1990, pp. 7200-7204. BibRef 9009

Lebegue, X., Aggarwal, J.K.,
Significant Line Segments for an Indoor Mobile Robot,
RA(9), 1993, pp. 801-815. BibRef 9300
And:
Detecting 3D Parallel Lines for Perceptual Organization,
ECCV92(720-724).
WWW Version. BibRef

Denasi, S., Quaglia, G., and Rinaudi, D.,
The Use of Perceptual Organization in the Prediction of Geometric Structures,
PRL(13), No. 7, 1991, pp. 529-539. BibRef 9100

Leclerc, Y.G.,
Region Grouping Using the Minimum-Description-Length Principle,
DARPA90(473-481). Group transparent surface regions together. (Some of the theory on human perception seems to say this only works one way, not the other?) BibRef 9000

Shashua, A., and Ullman, S.,
Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network,
ICCV88(321-327).
IEEE Abstract. IEEE Top Reference. BibRef 8800
And: A2, A1: MIT AI Memo-1061, July 1988. BibRef

Shashua, A., and Ullman, S.,
Grouping Contours by Iterated Pairing Network,
Neural Info(3), 1991, pp. 335-341, BibRef 9100

Borra, S.[Sudhir], Sarkar, S.[Sudeep],
A Framework for Performance Characterization of Intermediate Level Grouping Modules,
PAMI(19), No. 11, November 1997, pp. 1306-1312.
IEEE Abstract. IEEE Top Reference.
WWW Version. Code and images available:
HTML Version. 9712Compare (in order of ranking): Jacobs: See also Robust and Efficient Detection of Salient Convex Groups. Sarkar-Boyer: See also Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization. Etemadi: See also Low-Level Grouping of Straight Line Segments. BibRef

Feldman, J.[Jacob],
Perceptual Grouping by Selection of a Logically Minimal Model,
IJCV(55), No. 1, September 2003, pp. 5-25.
WWW Version. 0307 BibRef

Feldman, J.[Jacob],
Regularity-Based Perceptual Grouping,
CompIntel(13), No. 4, November 1997, pp. 582-623. 9801 BibRef
Earlier:
Efficient Regularity-Based Grouping,
CVPR97(288-294).
IEEE Abstract. IEEE Top Reference.
WWW Version. 9704Grouping, general. BibRef

Feldman, J.,
Constructing perceptual categories,
CVPR92(244-250).
IEEE Abstract. IEEE Top Reference. 0403 BibRef

Amir, A., Lindenbaum, M.,
A Generic Grouping Algorithm and Its Quantitative Analysis,
PAMI(20), No. 2, February 1998, pp. 168-185.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9803Grouping by graph clustering. Find lines and curves in noisy images. BibRef

Amir, A., Lindenbaum, M.,
Quantitative Analysis of Grouping Processes,
ECCV96(I:369-384).
WWW Version. BibRef 9600

Amir, A., Lindenbaum, M.,
Grouping-Based Nonadditive Verification,
PAMI(20), No. 2, February 1998, pp. 186-192.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9803 BibRef

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization in Computer Vision: Status, Challenges, and Potential,
CVIU(76), No. 1, October 1999, pp. 1-6.
WWW Version. Guest Editors' Introduction. Perceptual Grouping BibRef 9910

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization for Artificial Vision Systems,
KluwerMarch 2000, ISBN 0-7923-7799-0
WWW Version. BibRef 0003

Foresti, G.L., Regazzoni, C.S.,
A Hierarchical Approach to Feature Extraction and Grouping,
IP(9), No. 6, June 2000, pp. 1056-1074.
IEEE DOI may work or IEEE-CS DOI may work. 0006 BibRef

Luo, J.B.[Jie-Bo], Singhal, A.[Amit],
On Measuring Low-Level Self and Relative Saliency in Photographic Images,
PRL(22), No. 2, February 2001, pp. 157-169. 0101 BibRef
Earlier:
On Measuring Low-Level Saliency in Photographic Images,
CVPR00(I: 84-89).
IEEE Abstract. IEEE Top Reference.
WWW Version. 0005Seg. by Saliency BibRef

Mordohai, P.[Philippos], Medioni, G.[Gérard],
Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning,
Morgan Claypool2006. Synthesis Lectures on Image, Video, and Multimedia Processing
WWW Version. Survey, Tensor Voting. BibRef 0600

Medioni, G., Lee, M.S.[Mi-Suen], Tang, C.K.[Chi-Keung],
A Computational Framework for Segmentation and Grouping,
Elsevier2000. ISBN: 0-444-50353-6 BibRef 0001 USC Computer VisionConceptual framework that solves a wide variety of problems -- Tensor Voting.
WWW Version. BibRef

Johansen, P.[Peter], Ersbřll, B.K.[Bjarne K.],
Guest Editors' Introduction,
IJCV(42), No. 1-2, April-May 2001, pp. 5-5.
WWW Version. 0106 BibRef
And: JMIV(15), No. 1/2, July 2001, pp. 5-5. 0106Perceptual grouping. Papers in both journals. BibRef

Pauli, J.[Josef], Sommer, G.[Gerald],
Perceptual organization with image formation compatibilities,
PRL(23), No. 7, May 2002, pp. 803-817.
HTML Version. 0203 BibRef

Zweck, J.[John], Williams, L.R.[Lance R.],
Euclidean Group Invariant Computation of Stochastic Completion Fields Using Shiftable-Twistable Functions,
JMIV(21), No. 2, September 2004, pp. 135-154.
WWW Version. 0409 BibRef
Earlier: ECCV00(II: 100).
WWW Version. 0003 BibRef

Maeder, A.J.[Anthony J.],
The image importance approach to human vision based image quality characterization,
PRL(26), No. 3, February 2005, pp. 347-354.
WWW Version. 0501 BibRef

Maeder, A.J.[Anthony J.], Osberger, W.[Wilfried],
Automatic Identification of Perceptually Important Regions in an Image Using a Model of the Human Visual System,
ICPR98(Vol I: 701-704).
IEEE DOI may work or IEEE-CS DOI may work. Features used to find salient regions. BibRef 9800

Chen, H.T.[Hwann-Tzong], Liu, T.L.[Tyng-Luh], Fuh, C.S.[Chiou-Shann],
Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping,
IJCV(65), No. 1-2, November 2005, pp. 73-96.
WWW Version. 0604 BibRef
Earlier: A1 and A2 only, Add A3: Chang, T.L.[Tien-Lung], CVPR05(II: 369-376).
IEEE DOI may work or IEEE-CS DOI may work. 0507 BibRef

Feldman, T.[Thomas], Younes, L.[Laurent],
Homeostatic image perception: An artificial system,
CVIU(102), No. 1, April 2006, pp. 70-80.
WWW Version. Image model; Visual system; Gibbs distribution; Saliency detection 0604Complements PCA by analyzing interactions.
BibRef

Parvin, B., Yang, Q.[Qing], Han, J., Chang, H., Rydberg, B., Barcellos-Hoff, M.H.,
Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events,
IP(16), No. 3, March 2007, pp. 615-623.
IEEE DOI may work or IEEE-CS DOI may work. 0703 See also Tool for the Quantitative Spatial Analysis of Complex Cellular Systems, A. BibRef

Yang, Q.[Qing], Parvin, B., Barcellos-Hoff, M.H.,
Localization of saliency through iterative voting,
ICPR04(I: 63-66).
IEEE DOI may work or IEEE-CS DOI may work. 0409 BibRef

Hu, J.Y.[Jian-Ying], Mojsilovic, A.[Aleksandra],
High-utility pattern mining: A method for discovery of high-utility item sets,
PR(40), No. 11, November 2007, pp. 3317-3324.
WWW Version. 0707High-utility item sets; Pattern mining; Partition tree BibRef

Michaelsen, E.[Eckart], Middelmann, W.[Wolfgang], Sörgel, U.[Uwe],
Cognitive Vision and Perceptual Grouping by Production Systems with Blackboard Control: An Example for High-Resolution SAR-Images,
VISAPP06(293-304).
WWW Version. 0711 BibRef

Gao, D.[Dashan], Vasconcelos, N.[Nuno],
Bottom-up saliency is a discriminant process,
ICCV07(1-6).
IEEE DOI may work or IEEE-CS DOI may work. 0710 BibRef

Campadelli, P.[Paola], Lombardi, G.[Gabriele],
Tensor Voting Fields: Direct Votes Computation and New Saliency Functions,
CIAP07(677-684).
IEEE DOI may work or IEEE-CS DOI may work. 0709 BibRef

Hou, X.D.[Xiao-Di], Zhang, L.Q.[Li-Qing],
Saliency Detection: A Spectral Residual Approach,
CVPR07(1-8).
IEEE DOI may work or IEEE-CS DOI may work. 0706 BibRef

Syeda-Mahmood, T.[Tanveer], Wang, F.[Fei],
Unsupervised Clustering using Multi-Resolution Perceptual Grouping,
CVPR07(1-8).
IEEE DOI may work or IEEE-CS DOI may work. 0706 BibRef

Loss, L.[Leandro], Bebis, G.N.[George N.], Nicolescu, M.[Mircea], Skourikhine, A.N.[Alexei N.],
An Automatic Framework for Figure-Ground Segmentation in Cluttered Backgrounds,
BMVC07(xx-yy).
PDF Version. 0709 BibRef
Earlier:
Perceptual Grouping Based on Iterative Multi-scale Tensor Voting,
ISVC06(II: 870-881).
WWW Version. 0611 BibRef

Liu, Y.[Yang], Bouganis, C.S., Cheung, P.Y.K.,
A Spatiotemporal Saliency Framework,
ICIP06(437-440). 0610
IEEE DOI may work or IEEE-CS DOI may work. BibRef

Orabona, F.[Francesco], Metta, G.[Giorgio], Sandini, G.[Giulio],
Learning Association Fields from Natural Images,
PercOrg06(174).
IEEE DOI may work or IEEE-CS DOI may work. 0609 BibRef

Govindu, V.M.[Venu Madhav], Layout, S.[Simhapuri],
A Tensor Decomposition for Geometric Grouping and Segmentation,
CVPR05(I: 1150-1157).
IEEE DOI may work or IEEE-CS DOI may work. 0507Apply method to salient feature grouping and motion segmentation. BibRef

Driancourt, R.[Remi],
Learning Perceptual Organization with a Developmental Robot,
PercOrg04(60).
IEEE DOI may work or IEEE-CS DOI may work. 0502 BibRef

Arsenio, A.M.[Artur M.],
An Embodied Approach to Perceptual Grouping,
PercOrg04(51).
IEEE DOI may work or IEEE-CS DOI may work. 0502 BibRef

Engbers, E.A.[Erik A.], Lindenbaum, M.[Michael], Smeulders, A.W.M.[Arnold W.M.],
An Information-Based Measure for Grouping Quality,
ECCV04(Vol III: 392-404).
WWW Version. 0405 BibRef

Massad, A.,
A Perceptual Grouping Approach for Visual Interpolation between Good Continuation and Minimal Path using Tensor Voting,
BMVC06(II:639).
PDF Version. 0609 BibRef

Aziz, M.Z.[Muhammad Zaheer], Mertsching, B.[Bärbel],
An Attentional Approach for Perceptual Grouping of Spatially Distributed Patterns,
DAGM07(345-354).
WWW Version. 0709 BibRef

Massad, A., Babós, M., Mertsching, B.[Bärbel],
Application of the Tensor Voting Technique for Perceptual Grouping to Grey-Level Images,
DAGM02(306 ff.).
HTML Version. 0303 BibRef

Malik, J.,
Visual grouping and object recognition,
CIAP01(612-621).
IEEE Top Reference. 0210 BibRef

Yu, S.X.[Stella X.], Zhang, H.[Hao], Malik, J.[Jitendra],
Inferring spatial layout from a single image via depth-ordered grouping,
Tensor08(1-7).
IEEE DOI may work or IEEE-CS DOI may work. 0806 BibRef

Yu, S.X.[Stella X.],
Segmentation Induced by Scale Invariance,
CVPR05(I: 444-451).
IEEE DOI may work or IEEE-CS DOI may work. 0507handle texture and contours through scales. BibRef

Yu, S.X.[Stella X.], and Shi, J.B.[Jian-Bo],
Understanding Popout through Repulsion,
CVPR01(II:752-757).
IEEE Abstract. IEEE Top Reference. 0110 BibRef
And:
Understanding Popout: Pre-attentive Segmentation through Nondirectional Repulsion,
CMU-RI-TR-01-20, July, 2001.
PDF Version. 0205 BibRef
And:
Perceiving Shapes through Region and Boundary Interaction,
CMU-RI-TR-01-21, July, 2001.
PDF Version. 0205 BibRef

Mahoney, J.V.[James V.], Fromherz, M.P.J.[Markus P.J.],
Perceptual organization as graph rectification in a constraint-based scheme for interpreting sloppy stick figures,
PercOrg01(xx-yy). 0106 BibRef

Marques, J.S.[Jorge S.], Abrantes, A.J.[Arnaldo J.],
A Constrained Clustering Algorithm for Shape Analysis with Multiple Features,
ICPR00(Vol I: 916-919).
IEEE DOI may work or IEEE-CS DOI may work.
HTML Version. 0009 BibRef

Ambrosio, G.[Gregorio], González, J.[Javier],
Extracting and Matching Perceptual Groups for Hierarchical Stereo Vision,
ICPR00(Vol I: 542-545).
IEEE DOI may work or IEEE-CS DOI may work.
HTML Version. 0009 BibRef

Marichal, X.[Xavier], Delmot, T., de Vleeschouwer, C., Warscotte, V., Macq, B.,
Automatic Detection of Interest Areas of an Image or of a Sequence of Images,
ICIP96(III: 371-374).
IEEE DOI may work or IEEE-CS DOI may work. Saliency. Find salient regions in video. BibRef 9600

Sara, R.[Radim], and Bajcsy, R.[Ruzena],
Fish-Scales: Representing Fuzzy Manifolds,
ICCV98(811-817).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9800

Borra, S., Sarkar, S.,
Experimental Performance Evaluation of Feature Grouping Modules,
CVPR97(891-896).
IEEE Abstract. IEEE Top Reference.
WWW Version. 9704 BibRef

Serra, J.R., Subirana-Vilanova, J.B.,
Perceptual grouping on texture images using non-cartesian networks,
ICPR96(II: 462-466).
IEEE DOI may work or IEEE-CS DOI may work. 9608(Univ. Autonoma Barcelona, E) BibRef

Subirana, B.[Brian],
Perceptual Organization, Figure Ground, Attention And Saliency,
MIT AI Memo-1218, August 1991. BibRef 9108

Lawton, D.T., McConnell, C.C.,
Perceptual Organization Using Interestingness,
SPMSF87(405-419). BibRef 8700

Dabis, H.S., Palmer, P.L., Kittler, J.V.,
An Interest Operator Based on Perceptual Grouping,
SCIA95(315-322). BibRef 9500

Wang, C.L., Prasanna, V.K., Chung, Y.,
Parallel Implementations of Perceptual Grouping Tasks on Distributed Memory Machines,
ARPA96(905-912). BibRef 9600

Fellenz, W.A., Hartmann, G.,
Preattentive Grouping and Attentive Selection for Early Visual Computation,
ICPR96(IV: 340-345).
IEEE DOI may work or IEEE-CS DOI may work. 9608(Univ. of Paderborn, D) BibRef

Kang, H.B., Walker, E.L.,
Multilevel Grouping: Combining Bottom-Up and Top-Down Reasoning for Object Recognition,
ICPR94(A:559-562).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9400

Derou, D.[Dominique], Herault, L.[Laurent],
Pulsed neural networks and perceptive grouping,
ECCV94(A:521-526).
WWW Version. 9405 BibRef

Horaud, R., Veilon, F., and Skordas, T.,
Finding Geometric and Relational Structures in an Image,
ECCV90(374-384).
WWW Version. Group simple features into more comples structures. BibRef 9000

Subirana-Vilanova, J.B., and Sung, K.K.[Kah Kay],
Multi-Scale Vector-Ridge-Detection for Perceptual Organization Without Edges,
ICCV93(57-64).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9300
And: MIT AI Memo-1318, December 1992.
WWW Version. BibRef
Earlier:
Perceptual Organization without Edges,
DARPA92(289-298). Grouping using regions and using color for grouping. BibRef

Subirana-Vilanova, J.B.,
The Skeleton Sketch: Finding Salient Frames of Reference,
DARPA90(614-622). BibRef 9000

Subirana-Vilanova, J.B.,
Curved Inertia Frames and the Skeleton Sketch: Finding Salient Frames of Reference,
ICCV90(702-708).
IEEE DOI may work or IEEE-CS DOI may work. BibRef 9000

Abella, A.,
Extracting Geometric Shapes from a Set of Points,
DARPA92(573-583). Grouping applied to points. BibRef 9200

Ahmad, S.,
VISIT: An Efficient Computational Model of Human Visual Attention,
ICSITR-91-049, Berkeley, CA, 1991, BibRef 9100 Ph.D.Thesis (UofIll). BibRef

Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Grouping, Lines and Curves .