Approach to reducing the labeling cost of Markov random fields within an infinite label space

Signal Processing

Volumn 81, Issue 3, 2001, Pages 609-620

  Li, Chang Tsun ^a  

^a NATIONAL DEFENSE UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; IMAGE ANALYSIS; IMAGE QUALITY; IMAGE SEGMENTATION; SIMULATED ANNEALING;

MARKOV RANDOM FIELDS (MRF);

MARKOV PROCESSES;

EID: 0035276973     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-1684(00)00235-8     Document Type: Article

References (26)

1. P. Andrey, P. Tarroux, Unsupervised segmentation of Markov random field modeled textured images using selectionist relaxation, IEEE Trans. Pattern Anal. Mach. Intell. 20 (3) (March 1998) 252–262.
2. J. Besag Spatial interaction and the statistical analysis of lattice systems J. Roy. Stat. Soc. (Ser. B) 36 1974 192 236
3. C.A. Bouman, M. Shapiro, A multiscale random field model for Bayesian image segmentation, IEEE Trans. Image Process. 3 (2) (February 1994) 162–176.
4. O. Catoni Rough large deviation estimates for simulated annealing application to exponential schedules Ann. Probab. 20 3 1992 1109 1146
5. J.L. Chen, A. Kundu, Unsupervised texture segmentation using multi-channel decomposition and hidden Markov-models, IEEE Trans. Image Process. 4 (5) (May 1995) 603–619.
6. J.G. Choi S.-D. Kim Multi-stage segmentation of optical flow field Signal Processing 54 2 1996 109 118
7. F.S. Cohen, Z. Fan, Maximum likelihood unsupervised textured image segmentation, Comput. Vision Graphics Image Process. 54 (3) (May 1992) 239–251.
8. R.J. Elliott, L. Aggoun, Estimation for discrete Markov random fields observed in Gaussian noise, IEEE Trans. Inform. Theory IT-40 (September 1994) 1600–1603.
9. J.S. Evans, V.Krishnamurthy, Hidden Markov model state estimation with randomly delayed observations, IEEE Trans. Signal Process. 47 (8) (August 1999) 2157–2166.
10. J.J. Ford, J.B. Moore, On adaptive HMM state estimation, IEEE Trans. Signal Process. 46 (2) (February 1998) 475–486.
11. S. Geman, D. Geman, Stochastic relaxation, Gibbs distribution, and Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell. 6 (6) (November 1984) 721–741.
12. R.P. Grimaldi Discrete and Combinatorial Mathematics – an Applied Introduction 1994 Addison-Wesley Reading, MA (Chapter 11)
13. C. Kervrann, F. Heitz, A Markov random field model based approach to unsupervised texture segmentation using local and global spatial statistics, IEEE Trans. Image Process. 4 (6) (June 1995) 856–862.
14. R. Kinderman J.L. Snell Markov Random Fields and Their Applications 1980 American Mathematical Society Providence, RI
15. S. Krishnamachari, R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Trans. Image Process. 6 (2) (February 1997) 251–267.
16. J.M. Kroll, N.C. Phamdo, Analysis and design of Trellis codes optimized for a binary symmetric Markov source with MAP detection, IEEE Trans. Inform. Theory IT-44 (November 1998) 2977–2987.
17. C.T. Li, Unsupervised texture segmentation using multiresolution Markov random fields, Ph.D. Thesis, Department of Computer Science, The University of Warwick, UK, 1998.
18. S.Z. Li Markov Random Field Modeling in Computer Vision 1995 Springer Berlin
19. B.S. Manjunath, R. Chellappa, Unsupervised texture segmentation using Markov random field models, IEEE Trans. Pattern Anal. Mach. Intell. 13 (5) (May 1991) 478–482.
20. D.K. Panjwani, G. Healey, Markov random field models for unsupervised segmentation of textured color images, IEEE Trans. Pattern Anal. Mach. Intell. 17 (1995) 939–954.
21. C. Raphael, Automatic segmentation of acoustic musical signals using hidden Markov models, IEEE Trans. Pattern Anal. Mach. Intell. 21 (4) (April 1999) 360–370.
22. C.S. Regazzoni, G.L. Foresti, A. Gibbs, Markov random field model for active imaging at microwave frequencies, Signal Processing 47 (2) (February 1995) 169–185.
23. A. Rosenfeld A.C. Kak Digital Picture Processing 1982 Academic Press New York
24. P.J.M. van Laarhoven E.H.L. Aarts Simulated Annealing Theory and Applications. Mathematics and its Applications 1987 D. Reidel Publishing Company Dordrecht, Holland
25. J.-P. Wang, Stochastic relaxation on partitions with connected components and its application to image segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 20 (6) (June 1998) 619–626.
26. Z. Zhang, E.-H. Yang, An on-line universal lossy data compression algorithm via continuous codebook refinement – Part II. Optimality for phi-mixing source models, IEEE Trans. Inform. Theory, IT-42 (May 1996) 822–836.