A MULTILEVEL GMRF-BASED APPROACH TO IMAGE SEGMENTATION AND RESTORATION

In this paper, the Gibbs-Markov approach is extended to integration of observations provided by virtual sensors and organized according to a hierarchical taxonomy. The proposed extension is applied to image res toration and segmentation. A model of coupled Gibbs-Markov random fiel ds (GMRFs) is presented, which involves performing restoration and lab eling at two abstraction levels. i.e., the image (pixel) level and the region level. The maximum a posteriori (MAP) approach usually applied as an estimation criterion for single-level GMRFs is shown to be a sp ecial case of the most probable explanation (MPE) criterion, which is valid for multilevel GMRFs. A stochastic distributed optimization algo rithm is used to reach the solution.