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by Dalila Benboudjema, Wojciech Pieczynski, Y Ys
Benboudjema, W. Pieczynski / Computer Vision and Image Understanding 99 (2005) 476–498 497
http://www-citi.int-evry.fr/~pieczyn/C63.pdf
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Abstract:
Hidden Markov fields (HMF), which are widely applied in various problems arising in image processing, have recently been generalized to Pairwise Markov Fields (PMF). Although the hidden process is no longer necessarily a Markov one in PMF models, they still allow one to recover it from observed data. We propose in this paper two original methods of parameter estimation in PMF, based on general Stochastic Gradient (SG) and Iterative Conditional Estimation (ICE) principles, respectively. Some experiments concerning unsupervised image segmentation based on Bayesian Maximum Posterior Mode (MPM) are also presented. 1.
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