MAP region segmentation based on composite random field models
A composite of two random field models is used to describe the observed image: a high-level process that describes the various regions in the images is modeled by a Gibbs-Markov model, and a low-level process that describes each particular region is modeled by a simultaneous autoregressive model. Using this composition, a recursive maximum a posterior (MAP) segmentation algorithm is formulated and various issues related to parameter estimation are discussed.<<ETX>>