Toward a full probability model of edges in natural images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2350, Issue , 2002, Pages 328-342

  Pedersen, Kim S ^a   Lee, Ann B ^b  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b BROWN UNIVERSITY   (United States)

Author keywords

Biologically inspired computational models; Image features; Natural image statistics; Probability model of local geometry; Scale space

Indexed keywords

BIOMIMETICS; COMPUTATIONAL GEOMETRY;

COMPUTATIONAL MODEL; IMAGE FEATURES; LOCAL GEOMETRY; NATURAL IMAGE STATISTICS; SCALE SPACES;

COMPUTER VISION;

EID: 84944405740     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-47969-4_22     Document Type: Conference Paper

References (28)

1. G. Arfken. Mathematical Methods for Physicists. Computational Imaging and Vision. Academic Press, London, 1985.
2. A. J. Bell and T. J. Sejnowski. The ”independent components” of natural scenes are edge filters. Vision Research, 37:3327–3338, 1997.
3. A. Desolneux, L. Moisan, and J. Morel. Partial gestalts. Technical Report CMLA 2001-22, CMLA, Cachan, France, 2001.
4. D. J. Field. Relations between the statistics of natural images and the response proporties of cortical cells. J. Optic. Soc. of Am., 4(12):2379–2394, 1987.
5. L. Florack. Image Structure. Computational Imaging and Vision. Kluwer Academic Publishers, Dordrecht, 1997.
6. L. Florack, B. ter Haar Romeny, M. Viergever, and J. Koenderink. The gaussian scale-space paradigm and the multiscale local jet. IJCV, 18:61–75, 1996.
7. J. Huang, A. Lee, and D. Mumford. Statistics of range images. In Proc. of CVPR’00, 2000.
8. J. Huang and D. Mumford. Statistics of natural images and models. In Proc. of CVPR’99, 2000.
9. A. Hyvärinen. Survey on independent component analysis. Neural Computing Surveys, 2:94–128, 1999.
10. J. J. Koenderink. The structure of images. Biol. Cybern., 50:363–370, 1984.
11. J. J. Koenderink and A. J. van Doorn. Representation of local geometry in the visual system. Biol. Cybern., 55:367–375, 1987.
12. J. J. Koenderink and A. J. van Doorn. Metamerism in complete sets of image operators. In K. Bowyer and N. Ahuja, editors, Advances in Image Understanding, pages 113–129. IEEE Comp. Soc. Press, 1996.
13. A. B. Lee, K. S. Pedersen, and D. Mumford. The complex statistics of highcontrast patches in natural images. In WWW Proc. of 2nd Int. IEEE Workshop on Statistical and Computational Theories of Vision, Vancouver, Canada, 2001. http://www.cis.ohio-state.edu/˜szhu/SCTV2001.html.
14. A. B. Lee, K. S. Pedersen, and D. Mumford. The nonlinear statistics of highcontrast patches in natural images. Technical Report APPTS Report #01-3, Brown University, USA, 2001.
15. T. Lindeberg. Scale-Space Theory in Computer Vision. Kluwer, 1994.
16. J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. IJCV, 43(1):7–27, 2001.
17. S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transaction on PAMI, 11:674–693, 1989.
18. D. Marr. Vision. W. H. Freeman, New York, 1982.
19. D. Mumford and B. Gidas. Stochastic models for generic images. Quarterly of Applied Mathematics, 59(11):85–111, March 2001.
20. M. Nielsen and M. Lillholm. What do features tell about images? In Michael Kerckhove, editor, Scale-Space and Morphology in Computer Vision, LNCS 2106, pages 39–50, 2001.
21. B. A. Olshausen and D. J. Field. Natural image statistics and efficient coding. Network: Computation in Neural Systems, 7(2):333–339, May 1996.
22. D. Ruderman and W. Bialek. Statistics of natural images: Scaling in the woods. Physical Review Letters, 73(6):814–817, August 1994.
23. E. P. Simoncelli. Bayesian denoising of visual images in the wavelet domain. In P. Müller and B Vidakovic, editors, Bayesian Inference in Wavelet Based Models, pages 291–308. Springer-Verlag, New York, 1999.
24. E. P. Simoncelli. Modelling the joint statistics of images in the wavelet domain. In Proc. of SPIE, 44th Annual Meating, volume 3813, pages 188–195, Denver, 1999.
25. J. H. van Hateren and A. van der Schaaf. Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Soc. Lond. Series B, 265:359–366, 1998.
26. M. J. Wainwright and E. P. Simoncelli. Scale mixtures of gaussians and the statistics of natural images. In S. A. Solla et al., editor, Advances in Neural Information Processing Systems 12, pages 855–861. MIT Press, 2000.
27. C. Zetzsche, B. Wegmann, and E. Barth. Nonlinear aspects of primary vision: Entropy reduction beyond decorrelation. In Int’l Symposium, Society for Information Display, volume 24, pages 933–936, 1993.
28. S. C. Zhu and D. Mumford. Grade: Gibbs reaction and diffusion equations. — a framework for pattern synthesis, image denoising, and removing clutter. In Proc. of ICCV’98, 1998.