R. Azencott and E. , Simulated Annealing: Parallelization Techniques, 1992.

J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, pp.259-302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

B. Braathen, W. Pieczynski, and P. Masson, Global and local methods of unsuperivsed Bayesian segmentation of images, Machine Graph. Vis, vol.2, pp.39-52, 1993.

H. Caillol, W. Pieczynski, and A. Hillon, Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation, IEEE Transactions on Image Processing, vol.6, issue.3, pp.425-440, 1997.
DOI : 10.1109/83.557353

B. Chalmond, An iterative Gibbsian technique for reconstruction of m-ary images, Pattern Recognition, vol.22, issue.6, pp.747-761, 1989.
DOI : 10.1016/0031-3203(89)90011-3

R. Chellapa and R. L. Kashyap, Digital image restoration using spatial interaction models, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.30, issue.3, pp.461-472, 1982.
DOI : 10.1109/TASSP.1982.1163911

Y. Delignon, Etude statistique d'images radar de la surface de la mer, 1993.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc. B, vol.39, pp.1-38, 1977.

H. Derin and H. Elliot, Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.39-55, 1987.
DOI : 10.1109/TPAMI.1987.4767871

P. A. Devijver, Hidden Markov mesh random field models in image analysis, Advances in Applied Statistics, Statistics and Images: 1. Abingdon, U.K.: Carfax, pp.187-227, 1993.
DOI : 10.1007/978-3-642-83069-3_11

R. C. Dubes and A. K. Jain, Random field models in image analysis, J. Appl. Stat, vol.16, 1989.

S. Geman and G. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Machine Intell, vol.6, pp.721-741, 1984.

N. Giordana and W. Pieczynski, Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.5, pp.465-475, 1997.
DOI : 10.1109/34.589206

X. Guyon, Champs aléatoires sur un réseau, Collection Techniques Stochastiques, 1993.

R. Haralick and J. Hyonam, A Context Classifier, IEEE Transactions on Geoscience and Remote Sensing, vol.24, issue.6, pp.997-1007, 1986.
DOI : 10.1109/TGRS.1986.289563

R. L. Kashyap and R. Chellapa, Estimation and choice of neighbors in spatial-interaction models of images, IEEE Transactions on Information Theory, vol.29, issue.1, pp.60-72, 1983.
DOI : 10.1109/TIT.1983.1056610

P. A. Kelly, H. Derin, and K. D. Hartt, Adaptive segmentation of speckled images using a hierarchical random field model, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.36, issue.10, pp.1628-1641, 1988.
DOI : 10.1109/29.7551

S. Lakshmanan and H. Herin, Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.8, pp.799-813, 1989.
DOI : 10.1109/34.31443

J. Marroquin, S. Mitter, and T. Poggio, Probabilistic Solution of Ill-Posed Problems in Computational Vision, Journal of the American Statistical Association, vol.18, issue.397, pp.76-89, 1987.
DOI : 10.1080/01621459.1987.10478393

A. Marzouki, Segmentation statistique d'images radar, 1996.

A. Marzouki, Y. Delignon, and W. Pieczynski, Adaptive segmentation of SAR images, Proc. OCEAN '94

P. Masson and W. Pieczynski, SEM algorithm and unsupervised statistical segmentation of satellite images, IEEE Transactions on Geoscience and Remote Sensing, vol.31, issue.3, pp.618-633, 1993.
DOI : 10.1109/36.225529

E. Mohn, N. Hjort, and G. Storvic, A simulation study of some contextual clasification methods for remotely sensed data, GE-25, pp.796-804, 1987.

A. Peng and W. Pieczynski, Adaptive Mixture Estimation and Unsupervised Local Bayesian Image Segmentation, Graphical Models and Image Processing, vol.57, issue.5, pp.389-399, 1995.
DOI : 10.1006/gmip.1995.1033

W. Pieczynski and L. S. , Mixture of distributions, Markov random fields and unsupervised Bayesian segmentation of images Statistical image segmentation, Machine Graph. Vis, vol.28, issue.1, pp.261-268, 1990.

W. Qian and D. M. Titterington, On the use of Gibbs Markov chain models in the analysis of images based on second-order pairwise interactive distributions, Journal of Applied Statistics, vol.2, issue.2, pp.267-282, 1989.
DOI : 10.1109/TPAMI.1984.4767596

H. C. Quelle, J. Boucher, and W. Pieczynski, Adaptive parameter estimation and unsupervised image segmentation, Machine Graph. Vis, vol.5, pp.613-631, 1996.

R. A. Redner and H. F. Walker, Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, vol.26, issue.2, pp.195-239, 1984.
DOI : 10.1137/1026034

A. Rosenfeld and E. , Image Modeling, 1981.

J. Tilton, S. Vardeman, and P. Swain, Estimation of Context for Statistical Classification of Multispectral Image Data, IEEE Transactions on Geoscience and Remote Sensing, vol.20, issue.4, pp.445-452, 1982.
DOI : 10.1109/TGRS.1982.350410

A. Veijanene, A simulation-based estimator for hidden Markov random fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.8, pp.825-830, 1991.
DOI : 10.1109/34.85674

L. Younes, Parametric Inference for imperfectly observed Gibbsian fields, Prob. Theory Related Fields, pp.625-645, 1989.
DOI : 10.1007/BF00341287

J. Zerubia and R. Chellapa, Mean field annealing using compound Gauss-Markov random fields for edge detection and image estimation, IEEE Transactions on Neural Networks, vol.4, issue.4, pp.703-709, 1993.
DOI : 10.1109/72.238324

J. Zhang, The mean field theory in EM procedures for blind Markov random field image restoration, IEEE Transactions on Image Processing, vol.2, issue.1, pp.27-40, 1993.
DOI : 10.1109/83.210863

J. Zhang, J. W. Modestino, and D. A. Langan, Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation, IEEE Transactions on Image Processing, vol.3, issue.4, pp.404-420, 1994.
DOI : 10.1109/83.298395