Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

International Journal of Computer Vision

Volumn 27, Issue 2, 1998, Pages 107-126

  Zhu, Song Chun ^a   Wu, Yingnian ^b   Mumford, David ^c  

^a Stanford University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c BROWN UNIVERSITY   (United States)

Author keywords

Feature pursuit; Markov random field; Maximum entropy; Minimax entropy; Texture analysis and synthesis; Texture modeling; Visual learning

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DIGITAL FILTERS; FEATURE EXTRACTION; MARKOV PROCESSES; MATHEMATICAL MODELS; PROBABILITY; STATISTICAL METHODS; ENTROPY; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS; TEXTURES;

FILTERS RANDOM FIELDS AND MAXIMUM ENTROPY (FRAME); MARKOV RANDOM FIELDS (MRF); MINIMAX ENTROPY; STEPWISE ALGORITHMS; TEXTURE MODELS;

IMAGE ANALYSIS; COMPUTER VISION;

MARKOV RANDOM FIELDS; MINIMAX ENTROPY; TEXTURE MODELING; VISUAL LEARNING;

EID: 0032025550     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007925832420     Document Type: Article

References (36)

1. Barlow, H.B., Kaushal, T.P., and Mitchison, G.J. 1989. Finding minimum entropy codes. Neural Computation, 1:412-423.
2. Bergen, J.R. and Adelson, E.H. 1991. Theories of visual texture perception. In Spatial Vision, D. Regan (Eds.), CRC Press.
3. Besag, J. 1973. Spatial interaction and the statistical analysis of lattice systems (with discussion). J. Royal Stat. Soc., B, 36:192-236.
4. Besag, J. 1977. Efficiency of pseudolikelihood estimation for simple Gaussian fields. Biometrika, 64:616-618.
5. Chubb, C. and Landy, M.S. 1991. Orthogonal distribution analysis: A new approach to the study of texture perception. In Comp. Models of Visual Proc., M.S. Landy et al. (Eds.), MIT Press.
6. Coifman, R.R. and Wickerhauser, M.V. 1992. Entropy based algorithms for best basis selection. IEEE Trans, on Information Theory, 38:713-718.
7. Cross, G.R. and Jain, A.K. 1983. Markov random field texture models. IEEE, PAMI, 5:25-39.
8. Daubechies, I. 1992. Ten Lectures on Wavelets, Society for Industry and Applied Math: Philadephia, PA.
9. Daugman, J. 1985. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Journal of Optical Soc. Amer., 2(7).
10. Diaconis, P. and Freedman, D. 1981. On the statistics of vision: The Julesz conjecture. Journal of Math. Psychology, 24.
11. Donoho, D.L. and Johnstone, I.M. 1994. Ideal de-noising in an orthonormal basis chosen from a libary of bases. Acad. Sci. Paris, Ser. I. 319:1317-1322.
12. Field, D. 1987. Relations between the statistics of natural images and the response properties of cortical cells. J. of Opt. Soc. Amer., 4(12).
13. Gabor, D. 1946. Theory of communication. IEE Proc., 93(26).
14. Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. PAMI, 6:721-741.
15. Geman, S. and Graffigne, C. 1986. Markov random field image models and their applications to computer vision. In Proc. Int. Congress of Math., Berkeley, CA.
16. Geyer, C.J. and Thompson, E.A. 1995. Annealing Markov chain Monto Carlo with applications to ancestral inference. J. of Amer. Stat. Assoc., 90:909-920.
17. Haralick, R.M. 1979. Statistics and structural approach to texture. In Proc. IEEE, 67:786-804.
18. Heeger, D.J. and Bergen, J.R. 1995. Pyramid-based texture analysis/synthesis. Computer Graphics, in press.
19. Jain, A.K. and Farrokhsia, F. 1991. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24:1167-1186.
20. Jaynes, E.T. 1957. Information theory and statistical mechanics. Physical Review, 106:620-630.
21. Julesz, B. 1962. Visual pattern discrimination. IRE Trans, of Information Theory, IT-8:84-92.
22. Kullback, S. and Leibler, R.A. 1951. On information and sufficiency. Annual Math. Stat., 22:79-86.
23. Lee, T.S. 1992. Image representation using 2D Gabor wavelets. To appear in IEEE Trans, of Pattern Analysis and Machine Intelligence.
24. 2(R). Trans. Amer. Math. Soc., 315:69-87.
25. Mao, J. and Jain, A.K. 1992. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition, 25:173-188.
26. McCormick, B.H. and Jayaramamurthy, S.N. 1974. Time series models for texture synthesis. Int. J. Comput. Inform. Sci, 3:329-343.
27. Popat, K. and Picard, R.W. 1993. Novel cluster-based probability model for texture synthesis, classification, and compression. In Proc. SPIE Visual Comm., Cambridge, MA.
28. Qian, W. and Titterington, D.M. 1991. Multidimensional Markov chain models for image textures. J. Royal Stat. Soc., B, 53:661-674.
29. Silverman, M.S., Grosof, D.H., De Valois, R.L., and Elfar, S.D. 1989. Spatial-frequency organization in primate striate cortex. In Proc. Natl. Acad. Sci. U.S.A., 86.
30. Simoncelli, E.P., Freeman, W.T., Adelson, E.H., and Heeger, D.J. 1992. Shiftable multiscale transforms.IEEE Trans. on Information Theory, 38:587-607.
31. Tsatsanis, M.K. and Giannakis, G.B. 1992. Object and texture classification using higher order statistics. IEEE Trans on PAMI, 7:733-749.
32. Winkler, G. 1995. Image Analysis, Random Fields and dynamic Monte Carlo Methods, Springer-Verlag.
33. Witkin, A. and Kass, M. 1991. Reaction-diffusion textures. Computer Graphics, 25:299-308.
34. Yuan, J. and Rao S.T. 1993. Spectral estimation for random fields with applications to Markov modeling and texture classification. Markov Random Fields, Chellappa and Jain (Eds.), pp. 179-209.
35. Zhu, S.C. and Yuille, A.L. 1996. Region Competition: unifying snakes, region growing, and Bayes/MDL for multi-band image segmentation. IEEE Trans, on PAMI, 18(9).
36. Zhu, S.C., Wu, Y.N., and Mumford, D.B. 1996. Minimax entropy principle and its applications. Harvard Robotics Lab. Technique Report.