Bayes Smoothing Algorithms for Segmentation of Images Modelled by Markov Random Fields

摘要

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modelled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together with a criterion of optimality then determine a Bayes estimate of the scene. Examples are given where the algorithm is applied to test imagery and also SEASAT SAR imagery.