Exploring texture ensembles by efficient markov chain monte carlo-toward a "trichromacy" theory of texture

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 22, Issue 6, 2000, Pages 554-569

  Zhu, Song Chun ^a,b   Liu, Xiu Wen ^a   Wu, Ying Nian ^a  

^a OHIO STATE UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Gibbs ensemble; Julesz ensemble; Markov chain monte carlo; Texture modeling; Texture synthesis

Indexed keywords

ALGORITHMS; COMPUTER VISION; DATA STRUCTURES; IMAGE ANALYSIS; IMAGE QUALITY; INVERSE PROBLEMS; MONTE CARLO METHODS; OPTICAL FILTERS; PROBABILITY DISTRIBUTIONS; STATISTICAL METHODS;

GIBBS ENSEMBLES; JULESZ ENSEMBLES;

PATTERN MATCHING;

EID: 0034205013     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.862195     Document Type: Article

References (38)

1. H. Akaike, "On Entropy Maximization Principle," Applications of Statistics, P.R. Krishnaiah, éd., pp. 27-42, Amsterdam: North-Holland, 1977.
2. C.H. Anderson and W.D. Langer, "Statistical Models of Image Texture," Unpublished preprint, Washington University, St. Louis, Mo., 1996. (Send email to cha@shifter. wustl. edu to get a copy).
3. J. Besag, "Spatial Interaction and the Statistical Analysis of Lattice Systems," J. Royal Statistical Soc., Series B, vol. 36, pp. 192-236,1973.
4. J.A. Bucklew, Large Deviation Techniques in Decision, Simulation, and Estimation. New York: John Wiley, 1990.
5. T. Caelli, B. Julesz, and E. Gilbert, "On Perceptual Analyzers Underlying Visual Texture Discrimination: Part II," Biological Cybernetics, vol. 29, no. 4, pp. 201-214, 1978.
6. R. Chellapa and A. Jain eds. Markov Random Fields: Theory and Application. Academic Press, 1993.
7. C. Chubb and M.S. Landy, "Orthogonal Distribution Analysis: A New Approach to the Study of Texture Perception," Computer Models of Visual Processing, Landy et al. eds., MIT Press, 1991.
8. G.R. Cross and A.K. Jain, "Markov Random Field Texture Models," IEEE Trans. Pattern Analysis Machine Intelligence, vol. 5, pp. 25-39, 1983.
9. J.S. De Bonet and P. Viola, "A Non-Parametric Multi-Scale Statistical Model for Natural Images," Advances in Neural Information Processing, vol. 10, 1997.
10. I. Daubechies, Ten Lectures on Wavelets. Philadelphia: Society for Industry and Applied Math, 1992.
11. J. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters," J. Optical Soc. Am., vol. 2, no. 7, 1985.
12. A. Gagalowicz and S.D. Ma, "Model Driven Synthesis of Natural Textures for 3D Scenes," Computers and Graphics, vol. 10, pp. 161-170, 1986.
13. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distribu-tions and the Bayesian Restoration of Images," IEEE Trans. Pattern Analyst and Machine Intelligence, vol. 9, no. 7, pp. 721-741, 1984.
14. S. Geman and C. Graffigne, "Markov Random Field Image Models and their Applications to Computer Vision," Proc. Int'l Congress of Math., 1986.
15. H.O. Georgii, Gibbs Measures and Phase Transition. New York: de Gruyter, 1988.
16. W.R. Gilks and R.O. Roberts, "Strategies for Improving MCMC," Markov Chain Monte Carlo in Practice, W.R. Gilks et al. eds., chapter 6, Chapman and Hall, 1997.
17. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice. Chapman and Hall, 1997.
18. D.J. Heeger and J.R. Bergen, "Pyramid-Based Texture Analysis/ Synthesis," SIGGRAPHS, 1995.
19. B. Julesz, "Visual Pattern Discrimination," IRE Trans. Information Theory, vol. 8, pp. 84-92, 1962.
20. B. Julesz, "Toward an Axiomatic Theory of Preattentive Vision," pp. 585-612, Dynamic Aspects of Neocortical Function, G. Edelman et al. eds., New York: Wiley, 1984.
21. B. Julesz, Dialogues on Perception. MIT Press, 1995.
22. A. Kami and D. Sagi, " Where Practice Makes Perfect in Texture Discrimination-Evidence for Primary Visual Cortex Plasticity," Proc. Nat'1 Academy Science, U.S., vol. 88, pp. 4,966-4,970, 1991.
23. S. Kullback and R.A. Leibler, "On Information and Sufficiency," Ann. Math. Statistics, vol. 22, pp. 79-86, 1951.
24. J.S. Liu and Y.N. Wu, "Parameter Expansion for Data Augmenta-tion," J. Am. Statistical Assoc., vol. 94, pp. 1,254-1,274, 1999.
25. J.S. Liu and C. Sabatti, "Generalized Gibbs Sampler and Multigrid Monte Carlo for Bayesian Computation," Eiometrika, to appear.
26. D. Marr, Vision, New York: W.H. Freeman and Company, 1982.
27. R.W. Picard, I.M. Elfadel, and A.P. Pentland, "Markov/Gibbs Texture Modeling: Aura Matrices and Temperature Effects," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 371-377, 1991.
28. K. Popat and R.W. Picard, "Cluster Based Probability Model and its Application to Image and Texture Processing," IEEE Trans. Information Processing, vol. 6, no. 2, 1997.
29. J. Portilla and E.P. Simoncelli, "Texture Representation and Synthesis Using Correlation of Complex Wavelet Coefficient Magnitudes," Proc. IEEE Workshop Statistical and Computational Theories of Vision, 1999.
30. A. Treisman, "Features and Objects in Visual Processing," Scientific Am., Nov. 1986.
31. R. Vistnes, "Texture Models and Image Measures for Texture Discrimination," Int'l J. Computer Vision, vol. 3, pp. 313-336, 1989.
32. J. Weszka, C.R. Dyer, and A. Rosenfeld, "A Comparative Study of Texture Measures for Terrain Classification," IEEE Trans. System Man, and Cybernetics, vol. 6, Apr. 1976.
33. Y.N. Wu, S.C. Zhu, and X.W. Liu, "The Equivalence of Julesz and Gibbs Ensembles," Proc. Int'l Conf. Computer Vision, Sept. 1999.
34. A.L. Yuille and J.M. Coughlan, "Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 2, pp. 160-173, Feb. 2000.
35. S.C. Zhu, Y.N. Wu, and D. Mumford, "FRAME: Filters, Random Field and Maximum Entropy:-Towards a Unified Theory for Texture Modeling," Int'l J. Computer Vision, vol. 27, no. 2, pp. 1-20, Mar./Apr. 1998.
36. S.C. Zhu, Y.N. Wu, and D. Mumford, "Minimax Entropy Principle and Its Application to Texture Modeling," Neural Computation, vol. 9, no 8, Nov. 1997
37. S.C. Zhu and D. Mumford, "Prior Learning and Gibbs Reaction-Diffusion," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 11, Nov. 1997.
38. S. C. Zhu, "Embedding Gestalt Laws in Markov Random Fields," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 11, Nov. 1999.