A probabilistic method for extracting chains of collinear segments

Computer Vision and Image Understanding

Volumn 76, Issue 1, 1999, Pages 36-53

  Crevier, Daniel ^a  

^a Ophthalmos Systems Inc   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; MATRIX ALGEBRA; PROBABILITY DENSITY FUNCTION; RANDOM PROCESSES;

COLLINEAR CHAINS;

EDGE DETECTION;

EID: 0033204698     PISSN: 10773142     EISSN: None     Source Type: Journal    
DOI: 10.1006/cviu.1999.0785     Document Type: Article

References (21)

1. M. Wertheimer, Principles of perceptual organization, in Readings in Perception (D. Beardslee and M. Wertheimer, Eds.), pp. 115-135, Princeton Univ. Press, Princeton, NJ, 1958.
2. G. Kanisza, Organization in Vision, Praeger, New York, 1979.
3. A. Sha'ashua and S. Ullman, Structural saliency: the detection of globally salient structures using a locally connected network, in Proc. Int. Conf. on Computer Vision, Tampa, 1988, pp. 321-327.
4. P. Parent and S. W. Zucker, Trace inference, curvature consistency, and curve detection, IEEE Trans. Pattern Anal. Mach. Intell. 11, 1989, 823-839.
5. E. Saund, Symbolic construction of a 2-d scale-space image, IEEE Trans. Pattern Anal. Mach. Intell. 12(8), 1990, 817-830.
6. J. Dolan and E. Riseman, Computing curvilinear structure by token-based grouping, in Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1992.
7. D. G. Lowe, Perceptual Organization and Visual Recognition, Kluwer Academic, Boston/Dordrecht/Lancaster, 1985.
8. I. J. Cox, J. M. Rehg, and S. Hingorani, A bayesian multiple-hypothesis approach to edge grouping and contour segmentation, Internat. J. Comput. Vision 11(1), 1993, 5-24.
9. S. Sarkar and K. L. Boyer, Perceptual organization in computer vision: A review and a proposal for a classificatory structure, IEEE Trans. Systems Man Cybern. 23, 1993, 382-399.
10. S. Sarkar and K. L. Boyer, Integration, inference, and management of spatial information using bayesian networks: Perceptual organization, IEEE Trans. Pattern Anal. Mach. Intell. 15(3), 1993, 256-274.
11. R. L. Castano and S. Hutchinson, A probabilistic approach to perceptual grouping, Comput. Vision Image Understanding 64(3), 1996, 399-419.
12. D. W. Jacobs, Robust and efficient detection of salient convex groups, IEEE Trans. Pattern Anal. Mach. Intell. 18(1), 1996, 23-37.
13. J. H. Elder and S. W. Zucker, Computing contour closure, in Lecture Notes in Computer Science, Proc. 4th European Conf. on Computer Vision, pp. 399-412, Springer-Verlag, New York, 1996.
14. J. H. Elder and R. Goldberg, The statistics of natural image contours, in IEEE Computer Society Workshop on Perceptual Organization in Computer Vision, Santa Barbara, 1998.
15. J. H. Elder and R. M. Goldberg, Image editing in the contour domain, in Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, 1998, pp. 374-383.
16. J. Canny, A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 1986, 679-698.
17. A. Etemadi, Robust segmentation of edge data, in Proceedings, Fourth IEEE International Conference on Image Processing and Its Applications, April 7-9, Maastricht, The Netherlands, 1992, pp. 311-314.
18. D. Crevier and H. J. Yoo, A new method for straight line and circular arc extraction, in Proceedings, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, Vol. 2353, pp. 154-162, SPIE, Boston, 1994.
19. M. F. Barnsley, Fractals Everywhere, Academic Press, New York, 1993.
20. 2 power spectra and self-similar bandpass decompositions, in Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, 1998, pp. 86-91.
21. J. H. Elder and S. W. Zucker, A measure of closure, Vision Res. 34(24), 1994, 3361-3370.