A Multiscale Random Field Model for Bayesian Image Segmentation

IEEE Transactions on Image Processing

Volumn 3, Issue 2, 1994, Pages 162-177

  Bouman, Charles A ^a   Shapiro, Michael ^b  

^a PURDUE UNIVERSITY   (United States)

^b U S ARMY ENGINEER RESEARCH AND DEVELOPMENT CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; PARAMETER ESTIMATION; PROBABILITY; RANDOM PROCESSES; REMOTE SENSING;

BAYESIAN IMAGE SEGMENTATION; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI;

IMAGE PROCESSING;

EID: 0028392841     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/83.277898     Document Type: Article

References (47)

1. R. Haralick and L. Shapiro, “Image segmentation techniques,” Comput. Vision Graphics Image Processing, vol. 29, pp. 100–132, 1985.
2. C. Therrien, “An estimation-theoretic approach to terrain image segmentation,” Comput. Vision Graphics Image Processing, vol. 22, pp. 313–326, 1983.
3. J. Besag, “On the statistical analysis of dirty pictures,” J. Roy. Statist. Soc. B, vol. 48, no. 3, pp. 259–302, 1986.
4. H. Derin and H. Elliott, “Modeling and segmentation of noisy and textured images using Gibbs random fields,” IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-9, no. 1, pp. 39–55, Jan. 1987.
5. J. Besag, “Spatial interaction and the statistical analysis of lattice systems”, J. Roy. Statist. Soc. B, vol. 36, no. 2, pp. 192–236, 1974.
6. T. Pappas, “An adaptive clustering algorithm for image segmentation,” IEEE Trans. Signal Processing, vol. 40, no. 4, pp. 901–914, Apr. 1992.
7. K. Sauer and C. Bouman, “A local update strategy for iterative reconstruction from projections,” IEEE Trans. Signal Processing, Feb. 1993
8. M. Zhang, R. Haralick, and J. Campbell, “Multispectral image context classification using stochastic relaxation,” IEEE Trans. Syst. Man Cyber., vol. 20, no. 1, pp. 128–140, Feb. 1990.
9. B. Jeon and D. A. Landgrebe, “Spatio-temporal contextual classification based on Markov random field model,” in Proc. 1991 Int. Geosci. Remote Sensing Symp. (Espoo, Finland), 1991, pp. 1819–1822.
10. Z. Kato, J. Zerubia, and M. Berthod, “Satellite image classification using a modified Metropolis dynamics,” in Proc. IEEE Int. Conf. Acoust. Speech Signal ProcessingiSan Francisco, CA), Mar. 23–26, 1992, pp. 573–576, vol. 3.
11. B. Jeon and D. A. Landgrebe, “Classification with spatio-temporal interpixel class dependency contexts,” submitted to IEEE Trans. Geosci. Remote Sensing.
12. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Patt. Anal. Machine Intell., vol. 6, no. 6, pp. 721–741, Nov. 1984.
13. B. Gidas, “A renormalization group approach to image processing problems,” IEEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 2, pp. 164–180, Feb. 1989.
14. C. Bouman and B. Liu, “Segmentation of textured images using a multiple resolution approach,” Proc. IEEE Int. Conf. Acoust. Speech Signal Processing (New York), Apr. 11–14, 1988, pp. 1124–1127.
15. C. Bouman and B. Liu, “Multiple resolution segmentation of textured images,” IEEE Trans. Patt. Anal. Machine Intell., vol. 13, no. 2, pp. 99–113, Feb. 1991.
16. P. Perez and F. Heitz, “Multiscale markov random fields and constrained relaxation in low level image analysis,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Processing (San Francisco, CA), Mar. 23–26, 1992, pp. 61–64, vol. 3.
17. A. Pentland, “Fractal-based description of natural scenes,” IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-6, pp. 661–674, Nov. 1984.
18. S. Peleg, J. Naor, R. Hartley, and D. Avnir, “Multiple resolution texture analysis and classification,” IEEE Trans. Patt. Anal. Machine Intell., vol. PAMI-6, no. 4, pp. 518–523, July 1984.
19. J. Marroquin, S. Mitter, and T. Poggio, “Probabilistic solution of ill-posed problems in computational vision,” J. Amer. Stat. Assoc. vol. 82, pp 76–89, Mar. 1987.
20. R. Dubes, A. Jain, S. Nadabar, and C. Chen, “MRF model-based algorithms for image segmentation,” inProc. 10th Int. Conf. Patt. Recogn. (Atlantic City, NJ), June 1990, pp. 808–814.
21. D. Pickard, “Inference for discrete Markov fields: The simplest nontrivial case,” J. Amer. Stat. Assoc., vol. 82, pp. 90–96, Mar. 1987.
22. S. Lakshmanan and H. Derin, “Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing,” IEEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 8, pp. 799–813, Aug. 1989.
23. M. Luettgen, W. Karl, A. Willsky, and R. Tenney, “Multiscale representations of Markov random fields,” submitted to IEEE Trans. Signal Processing Special Issue on Wavelets and Signal Processing.
24. K. Chou, A. Willsky, A. Benveniste, and M. Basseville, “Recursive and iterative estimation algorithms for multi-resolution stochastic processes,” inProc. 28th Conf. Decision Contr. (Tampa, FL), Dec. 13–15, 1989, pp. 1184–1189, vol. 2.
25. K. Chou, S. Golden, and A. Willsky, “Modeling and estimation of multiscale stochastic processes,” inProc. IEEE Int. Conf. Acoust. Speech Signal Processing (Toronto), May 14–17, 1991, pp. 1709–1712.
26. M. Basseville et al., “Modeling and estimation of multiresolution stochastic processes,” IEEE Trans. Inform. Theory, vol. 38, no. 2, pp. 766–784, Mar. 1992.
27. M. Basseville, A. Benveniste, and A. Willsky, “Multiscale autoregressive processes, Part I: Schur-Levinson parametrizations,” IEEE Trans. Signal Processing, vol. 40, no. 8, pp. 1915–1934, Aug. 1992.
28. M. Basseville, A. Benveniste, and A. Willsky, “Multiscale autoregressive processes, Part II: Lattice structures for whitening and modeling,” IEEE Trans. Signal Processing, vol. 40, no. 8, pp. 1935–1954, Aug. 1992.
29. K. Chou, S. Golden, M. Luettgen, and A. Willsky, “Modeling and estimation of multiresolution stochastic processes and random fields,” in Proc. Seventh Workshop Multidimensional Signal Processing (Lake Placid, NY), Sept. 23–25, 1991, p. 3.8.
30. P. Burt, T. Hong, and A. Rosenfeld, “Segmentation and estimation of image region properties through cooperative hierarchical computation,” IEEE Trans. Patt. Anal. Machine Intell., vol. SMC-11, no. 12, pp. 802–809, Dec. 1981.
31. H. Antonisse, “Image segmentation in pyramids,” Comput. Vision Graphics Image Processing, vol. 19, pp. 367–383, 1982.
32. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann, 1988.
33. A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Roy. Statist. Soc. B, vol. 39, no. 1, pp. 1–38, 1977.
34. J. Westervelt, M. Shapiro, and D. P. Gerdes, GRASS Version 4.1 User's Reference Manual, US Army Construction Eng. Res. Labs,. Office of GRASS Integration, Champain IL, ADP Rep. under preparation 1993.
35. B. Manjunath, T. Simchony, and R. Chellappa, “Stochastic and deterministic networks for texture segmentation,” IEEE Trans. Acoust. Speech Signal Processing, vol. 38, no. 6, pp. 1039–1049, June 1990.
36. K. I. Laws, “Textured image segmentation,” Ph.D. dissertation, Dept. Eng., Univ. Southern Calif., Los Angeles, 1980.
37. M. Unser and M. Eden, “Multiresolution feature extraction and selection for texture segmentation,” IEEE Trans. Patt. Anal. Machine Intell., vol. 11, no. 7, pp 717–728, July 1989.
38. M. Unser and M. Eden, “Nonlinear operators for improving texture segmentation based on features extracted by spatial filtering,” IEEE Trans. Syst. Man Cyber., vol. 20, no. 4, pp. 804–815, July/Aug. 1990.
39. D. Landgrebe, “The development of a spectral-spatial classifier for earth observational data,” Patt. Recogn., vol. 12, pp. 165–175, 1980.
40. R. Kettig and D. Landgrebe, “Classification of multispectral image data by extraction and classification of homogeneous objects,” IEEE Trans. Geosci. Electron., vol. GE-14, no. 1, pp. 19–26, Jan. 1976.
41. H. Derin and W. Cole, “Segmentation of textured images using Gibbs random fields,” Comput. Vision Graphics Image Processing, vol. 35, pp. 72–98, 1986.
42. L. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains,” Ann. Math. Statist., vol. 41, no. 1, pp. 164–171, 1970.
43. W. Press, B. Flannery, S. Teukolsky, and Vetterling, Numerical Recipes in C: The Art of Scientific Computing. Cambridge, UK: Cambridge University Press, 1988.
44. C. Wu, “On the convergence properties of the EM algorithm,” Annals Statist., vol. 11, no. 1, pp. 95–103, 1983.
45. E. Redner and H. Walker, “Mixture densities, maximum likelihood and the EM algorithm,” SIAM Rev., vol. 26, no. 2, Apr. 1984.
46. D. Tazik et al., “U.S. Army land condition-trend analysis (LCTA) plot inventory field methods,” USACERL Tech. Rep. N-92/03, Feb. 1992.
47. J. Rissanen, “A universal prior for integers and estimation by minimum description length,” Annals Statist., vol. 11, no. 2, pp. 417–431, 1983.