Image Processing Algorithms Based on Finite-State Gibbs Models

Abstract

Gibbs (Markov) random fields are used as stochastic picture models in image processing because of their conceptual simplicity and due to the fact that Gibbs models are fit to synthesize algorithms based on Bayes approach. In this paper, we are concerned with Gibbs fields taking on values from finite sets. This restriction allows to overcome difficulties in estimating Gibbs distribution parameters and to synthesize some useful algorithms of image processing.