Multiresolution image segmentation

Abstract

In this paper we present a new algorithm for segmentation of noisy or textured images using a multiresolution Bayesian approach. Our algorithm is different from previously proposed multiresolution segmentation techniques in that we use a multiresolution Gaussian autoregressive (AR) model for the pyramid representation of the observed image. Our algorithm also approximates the "maximization of the posterior marginals" (MPM) estimate of the pixel class labels at each resolution, from coarsest to finest, unlike previously proposed techniques, which have been based on MAP estimation. Experimental results are presented to demonstrate the performance of the new algorithm.