A probabilistic approach to perceptual grouping

Computer Vision and Image Understanding

Volumn 64, Issue 3, 1996, Pages 399-419

  Castaño, Rebecca L ^a   Hutchinson, Seth ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALCULATIONS; IMAGE SEGMENTATION; PARAMETER ESTIMATION; PROBABILITY;

IMAGE FEATURES;

IMAGE ANALYSIS;

EID: 0030285179     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.1996.0068     Document Type: Article

References (47)

1. S. Ahmad, VISIT: An Efficient Computational Model of Human Visual Attention, Technical Report TR-91-049, Int'l Computer Science Institute, Berkeley, CA, 1991.
2. N. Ahuja and M. Tuceryan, Extraction of early perceptual structure in dot patterns: Integrating region, boundary, and component gestalt, Comput. Vision Graph Image Process. 48, 1989, 304-356.
3. D. H. Ballard and C. M. Brown, Computer Vision, Prentice-Hall, Englewood Cliffs, NJ, 1982.
4. H. G. Barrow and J. M. Tenebaum, Interpreting line drawings as three-dimensional surfaces, Artif. Intell. 17, 1981, 75-116.
5. I. Biederman, Recognition-by-components: A theory of human image understanding, Psych. Rev. 94(2), 1987, 115-147.
6. M. Boldt, R. Weiss, and E. Riseman, Token-based extraction of straight lines, IEEE Trans. Systems ManCybern. 19(6), 1989, 1581-1594.
7. J. B. Burns, A. R. Hanson, and E. M. Riseman, Extracting straight lines, IEEE Trans. Pattern Anal. Mach. Intell. 8(4), 1986, 425-455.
8. J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 1986, 679-698.
9. R. L. Castaño, A Probabilistic Framework for Grouping Image Features, Master's thesis, University of Illinois at Urbana-Champaign, 1994.
10. I. J. Cox, J. M. Rehg, and S. Hingorani, A Bayesian multiple hypothesis approach to edge grouping and contour segmentation, technical report, NEC, 1993.
11. R. O. Duda and P. E. Hart, Use of the Hough transform to detect lines and curves in pictures, Commun. ACM 15, 1972, 11-15.
12. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
13. G. Foresti, V. Murino, C. S. Regazzoni, and G. Vernazza, Grouping of rectilinear segments by the labeled Hough transform, Comput. Vision Graph. Image Process. 58, 1994, 22-42.
14. S. A. Friedberg, Finding axes of skewed symmetry, Comput. Vision Graph Image Process. 34, 1986, 138-155.
15. S. Geman, Experiments in Bayesian image analysis, Bayesian Statist. 3, 1988, 159-172.
16. R. Glachet, J. T. Lapreste, and M. Dhome, Locating and modelling a flat symmetric object from a single perspective image, CVGIP: Image Understanding, 57(2), 1993, 219-226.
17. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, Vol. 1, Addison-Wesley, Reading, MA, 1992.
18. D. H. Hubel and T. N. Wiesel, Receptive fields and functional architecture of monkey striate cortex, J. Physiol. 195, 1968, 215-242.
19. J. N. Huddleston and J. Ben-Arie, Grouping edgels into structural entities using circular symmetry, the distributed Hough transform, and probabilistic non-accidentalness, CVGIP: Image Understanding 57(2), 1993, 227-242.
20. K. Kanatani, Geometric Computation for Machine Vision, Oxford Univ. Press, Oxford, 1993.
21. G. Kanizsa, Organization in Vision, Praeger, New York, 1979.
22. S. M. LaValle and S. Hutchinson, A framework for constructing probability distributions on the space of image segmentations, Comput. Vision Image Understanding 61(2), 1995, 203-230.
23. D. G. Lowe, Three-dimensional object recognition from single two-dimensional images, Artif. Intell. 31, 1987, 355-395.
24. G. Marola, On the detection of the axes of symmetry of symmetric and almost symmetric planar images, IEEE Trans. Pattern Anal. Mach. Intell. 11(1), 1989, 104-108.
25. D. Marr, Vision, Freeman, New York, 1982.
26. R. Mohan and R. Nevatia, Perceptual organization for scene segmentation and description, IEEE Trans. Pattern Anal. Mach. Intell. 14(6), 1992, 616-635.
27. P. F. M. Nacken, A metric for line segments, IEEE Trans. Pattern Anal. Mach. Intell. 15(12), 1993, 1312-1318.
28. V. S. Nalwa, Line-drawing interpretation: Bilateral symmetry, IEEE Trans. Pattern Anal. Mach. Intell. 11(10), 1989, 1117-1120.
29. R. Nevatia and K. Ramesh Babu, Linear feature extraction and description, Comput. Vision Graph Image Process. 13, 1980, 257-269.
30. I. Rock, The Logic of Perception, MIT Press, Cambridge, MA, 1983.
31. H. Rom and G. Medioni, Hierarchical decomposition and axial shape description, in Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, 1992, pp. 49-55.
32. H. Rom and G. Medioni, Hierarchical decomposition and axial shape description, technical report, University of Southern California, Los Angeles, CA, 1992.
33. K. J. Moroney, S. M. LaValle, and S. A. Hutchinson, Methods for numerical integration of high-dimensional posterior densities with application to statistical image models, in Proceedings, SPIE Conf. on Neural and Stochastic Methods in Image and Signal Processing, 1993, pp. 292-303.
34. S. Sarkar and K. Boyer, Integration, inference, and management of spatial information using bayesian networks: Perceptual organization, IEEE Trans. Pattern Anal. Mach. Intell. 15(3), 1993, 256-274.
35. S. Sarkar and K. Boyer, Perceptual organization in computer vision: A review and a proposal for a classificatory structure, IEEE Trans. Systems Man Cybern. 23(2), 1993, 382-399.
36. S. Sarkar and K. Boyer, Computing Perceptual Organization in Computer Vision, World Scientific, Teaneck, NJ, 1994.
37. E. Saund, Symbolic construction of a 2-d scale-space image, IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 1986, 156-173.
38. A. Scher, M. Shneier, and A. Rosenfeld, Clustering of collinear line segments, Pattern Recognit. 15(2), 1982, 85-91.
39. L. Spillman and J. S. Werner, Visual Perception: The Neurophysiological Foundations. Academic Press, San Diego, 1990.
40. H. Stark and J. W. Woods, Probability, Random Processes, and Estimation Theory for Engineers, Prentice-Hall, Englewood Cliffs, NJ, 1994.
41. A. Treisman and S. Gormican, Feature analysis in early vision: Evidence from search asymmetries, Psychol. Rev. 95(1), 1988, 15-48.
42. J. Tsotsos, Analyzing vision at the complexity level, Behav. Brain Sci. 13, 1990, 423-469.
43. D. Van Essen and C. H. Anderson, Information processing strategies and pathways in the primate retina and visual cortex, in An Introduction to Neural and Electronic Networks (S. F. Zornetzer, J. L. Davis, and C. Lau, Eds.), Academic Press, San Diego, 1990.
44. M. Wertheimer, Principles of perceptual organization, in D. Beardslee and M. Wertheimer, editors, Readings in Perception, pp. 115-135, VanNostrand, Princeton, NJ, 1958.
45. A. Witkin and J. Tenebaum, On the role of structure in vision, in Human and Machine Vision (J. Beck, B. Hope, and A. Rosenfeld, Eds.), pp. 481-543, Academic Press, San Diego, 1983.
46. T. Zielke, M. Brauckmann, and W. von Seelen, Intensity and edge-based symmetry detection with an application to car-following, Comput. Vision Graph Image Process. 58(2), 1993, 177-190.
47. S. W. Zucker, The diversity of perceptual grouping, in Vision, Brain, and Cooperative Computation, pp. 231-261, MIT Press, Cambridge, MA, 1990.