Restoring hidden non stationary process using triplet partially markov chain with long memory noise

The hidden Markov chains (HMC), which are widely used in different data restoration problems, have recently been generalized to pairwise partially Markov chains (PPMC), in which the distribution of the observed chain conditional on the hidden one is of any form. In particular, long-memory noise cases can be dealt with. The aim of this paper is to propose a parameter estimation method and to show, via experiments, that unsupervised PPMC based image segmentation can perform better, when the noise is a long-memory one, than the classical HMC based methods