Unsupervised image segmentation using triplet Markov fields

Hidden Markov fields (HMF) models are widely applied it) Narious problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of I I M F is mainly due to (lie fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, which facilitates different processing strategic,,, such its Bayesian restoration. HMF have been recently generalized to "pairwise" Markov fields (PMF), which offer similar processing advantages and superior modeling capabilities, In PMF one directly assumes the Markovianity of the pair (X, Y). Afterwards. "triplet" Markov fields (TMF), ill which the distribution of the pair (X, Y) is the marginal distribution of a Markov field M U, 1), where U is all auxiliary process. have been proposed and still allow restoration processing. The aim of this paper is to propose it new parameter estimation method adapted to TMF, and to study the corresponding unsupervised image segmentation methods. The hitter are validated via experiments and real image processing, (c) 2005 Elsevier Inc. All rights reserved.