Semi-Markov random field models for image segmentation

In this paper we examine the problem of image segmentation of noisy images. We consider a doubly stochastic image model. The image is assumed to be the sum of the realizations of two independent random fields: the uncorrupted image and the noise field, consisting of independent, identically distributed, Gaussian random variables. The image segmentation technique employed here is a technique in which the image is represented by a semi-Markov random field corrupted by additive white noise. An adaptive Bayesian parameter estimation/image detection algorithm is developed. This algorithm allows us to estimate the unknown image and its underlying parameters in an optimal manner. We demonstrate the potential of the proposed algorithm in the case of the smoothing/segmentation of two 4-gray level real images.