Parameter estimation in Markov random field image modeling with imperfect observations. A comparative study

Pattern Recognition Letters

Volumn 24, Issue 14, 2003, Pages 2377-2389

  Ibanez M V ^a   Simo A ^a  

^a UNIVERSITAT JAUME I   (Spain)

Author keywords

MCMC methods; Monte Carlo likelihood; MRF models; Parameter estimation; Simulation study; Unsupervised image analysis

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MARKOV PROCESSES; PARAMETER ESTIMATION;

NORMALIZING CONSTANTS;

IMAGE ANALYSIS;

IMAGE ANALYSIS; MARKOV CHAIN; MONTE CARLO ANALYSIS; UNSUPERVISED CLASSIFICATION;

EID: 0041339533     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(03)00067-9     Document Type: Article

References (50)

1. Aykroyd R.G. Green P. Global and local priors, and the location of lesions using gamma-camera imagery Philos. Trans. Roy. Soc. London, Ser. A 337 1991 323-342
2. Barker S. Rayner P. Unsupervised image segmentation using Markov random field models Pattern Recognition 33 2000 587-602
3. Besag J. Statistical analysis of non-lattice lattice data Statistician 24 1975 179-195
4. Besag J. On the statistical analysis of dirty pictures J. Roy. Statist. Soc. Ser. B 48 1986 259-302
5. Cai J. Liu Z.-Q. Pattern recognition using Markov random field models Pattern Recognition 35 2002 725-733
6. Celeux G. Diebolt J. The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem Comput. Statist. Quart. 2 1985 73-82
7. Chalmond B. An iterative Gibbsian technique for reconstruction of Mary image Pattern Recognition 15 1989 115-129
8. Champagnat F. Goussard Y. Idier J. Unsupervised deconvolution of sparse spike train using stochastic approximation IEEE Trans. Signal Process. 44 1996 2988-2998
9. Chellappa R. Jain A. Markov Random Fields. Theory and Application 1991 Academic Press London
10. Cressie N. Statistics for Spatial Data Wiley Series in Probability and Mathematical Statistics 1993
11. Dempster A. Laird N. Rubin D. Maximum likelihood from incomplete data via the EM algorithm J. Roy. Statist. Soc. Ser. B 39 1977 1-38
12. Diebolt J. Ip E. Stochastic EM: Method and application Gilks W. Richardson S. Spiegelhalter D.J. Markov Chain Monte Carlo in Practice 1996 259-273 Chapman and Hall London
13. Domingo J. Ayala G. Simó A. Ves E.D. Martnez-Costa L. Marco P. Irregular motion recovery in fluorescein angiograms Pattern Recognition Lett. 18 8 1997 805-821
14. Gelfand A. Carlin B. Maximum-likelihood estimation for constrained- or missing-data models Can. J. Statist. 21 1993 303-311
15. Geman D. Bayesian image analysis by adaptive annealing IGARSS'85, Octubre 1985 1985 269-277 Springer-Verlag Amherst, USA
16. Geman S. Geman D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images IEEE Trans. PAMI 6 1984 721-741
17. Geman, S., McClure, D., 1987. Statistical methods for tomographic image reconstruction. In: Proc. 46th Sess. Inst. Statist. Inst. Bull. ISI, vol. 52
18. Geyer C. Likelihood inference for spatial point processes Barndorff-Nielsen O. Kendall W. Lieshout M.N.M. Stochastic Geometry 1999 79-140 Chapman and Hall/CRC Boca Raton, FL
19. Geyer C. Thompson E. Constrained Monte Carlo maximum likelihood for dependent data (with discussion) J. Roy. Statist. Soc. Ser. B 54 1992 657-699
20. Gilks W. Richardson S. Spiegelhalter D.J. Introducing Markov chain Monte Carlo Gilks W. Richardson S. Spiegelhalter D.J. Markov Chain Monte Carlo in Practice 1996 1-20 Chapman and Hall London
21. Gravier, G., Sigelle, M., Chollet, G., 2000. A Markov random field model for automatic speech recognition. In: Sanfeliu, A., Villanueva, J., Vanrell, M., Alquezar, R., Huang, T., (Eds.), Proc. 15th Internat. Conf. on Pattern Recognition, vol. 3, pp. 258-261
22. Green P. Bayesian reconstruction from emission tomography data using a modified em algorithm IEEE Trans. Med. Imaging 9 1990 84-93
23. Guyon X. Random Fields on a Network. Modeling Statistics and Applications 1995 Springer-Verlag
24. Ibánez M. Simó A. Bayesian detection of the fovea in eye fundus angiographies Pattern Recognition Lett. 20 1999 229-240
25. Jalobeanu A. Blanc-éraud L. Zerubia J. Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method Pattern Recognition 35 2002 341-352
26. Joyeux L. Boukir S. Besserer B. Buisson O. Reconstruction of degraded image sequences. Application to film restoration Image Vision Comput. 19 2001 503-516
27. Karssemeijer N. Application of Bayesian methods to segmentation in medical images Barone P.F. Piccioni M. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis Lecture Notes in Statistics vol. 74 1992 Springer-Verlag
28. Kato, Z., Zerubia, J., Berthod, M., 1995. Unsupervised parallel image classification using a hierarchical Markovian model. Tech. Rep. Research Report No. 2528. URL citeseer.nj.nec.com/kato95unsupervised.html
29. Kato Z. Zerubia J. Berthod M. Unsupervised parallel image classification using Markovian models Pattern Recognition 32 1999 591-604
30. Lakshamanan S. Derin H. Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing IEEE Trans. PAMI 11 1989 793-813
31. Li S. Markov Random Field Modeling in Computer Vision 1995 Springer Berlin
32. Lucke, H. Improved acoustic modeling for speech recognition using 2D Markov random fields. In: ICASSP'95, May 1995, Detroit, MI, pp. 540-543
33. Mateos J. Katsaggelos K.R.M. A Bayesian approach for the estimation and transmission of regularization parameters for reducing blocking artifacts IEEE Trans. Image Process. 9 2000 1200-1215
34. Mignotte M. Collet C. Pérez P. Bouthemy P. Markov random field and fuzzy logic modeling in sonar imagery: Application to the classification of underwater floor Computer Vision and Image Understanding 79 1 2000 4-24
35. Molina R. Katsaggelos K. Mateos J. Bayesian and regularization methods for hyperparameter estimation in image restoration IEEE Trans. Image Process. 8 1999 231-246
36. Mouria-beji F. A hierarchical Bayesian model for continuous speech recognition Pattern Recognition Lett. 23 2002 773-781
37. Nielsen S. On simulated EM algorithms J. Econ. 96 2000 267-292
38. Noda H. Shirazi M. Kawaguchi E. Mrf-based texture segmentation using wavelet descomposed images Pattern Recognition 35 2002 771-782
39. Pieczynski, W. Statistical image segmentation. In: GKPO'92, May 1992
40. Quintana F. Liu J. del Pino G.E. Monte Carlo EM with importance reweighting and its applications in random effects models Comput. Statist. Data Anal. 29 1999 429-444
41. Ruud P. Extensions of estimation methods using the EM algorithm J. Econom. 49 1991 305-341
42. Salzenstein F. Pieczynski W. Parameter estimation in hidden fuzzy Markov random fields and image segmentation CVGIP: Graph. Models Image Process. 59 1997 205-220
43. Saquib S. Bouman C. Sauer K. M.l. Parameter estimation for Markov random fields, with applications to Bayesian tomography IEEE Trans. Image Process. 7 1998 1029-1044
44. Simó A. de Ves E. Segmentation of macular fluorescein angiographies Pattern Recognition 34 4 2001 795-809
45. Teles M. Bayesian estimation of ventricular contours in angiographic images IEEE Trans. Med. Imaging 11 3 1992 416-429
46. Weir I. Fully Bayesian reconstruction from single photon emission computed tomography data J. Amer. Statist. Assoc. 92 437 1997 49-60
47. Weir I. Green P. Modeling data from single-photon emission computed tomography Mardia K. Statistics and Images 1994 313-338
48. Yao K. Mignotte M. Collet C. Galerne P. Burel G. Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery Pattern Recognition 33 2000 1575-1584
49. Younes L. Estimation and annealing for Gibbsian fields Annales de l'Institut Henri Poincaré. Probabilités et Statisitiques 24 1988 269-294
50. Younes L. Parameter estimation for imperfectly observed Gibbs fields and some comments on Chalmond's em Gibbsian algorithm Stochastic Models, Statistical Methods and Algorithms in Image Analysis Lectures Notes in Statistics vol. 47 1991 240-258 Springer