Unsupervised Segmentation Of Non Stationary Images With Non Gaussian Correlated Noise Using Triplet Markov Fields And The Pearson System

Abstract

The hidden Markov field (HMF) model has been used in many model-based solutions for image segmentation, and generally gives satisfying results. However, when the class image is non stationary, the unsupervised segmentation results provided by HMF can be poor. In this paper, we propose a new model based on triplet Markov fields (TMF) and the Pearson system which enables one to deal with non stationary hidden fields and correlated, possibly non Gaussian noise. Moreover, the nature of marginal distributions of the noise can vary with the class. We specify a new general parameter estimation method and apply it to unsupervised Bayesian image segmentation.