A parallel robust segmentation algorithm using the maximin method for MRF texture images

A robust algorithm is proposed for the segmentation of a special class of texture images where the images are composed of regions covered with two distinct fine textures. We describe the region images with Markov random fields (MRFs) and assume that the covering fine textures are realizations of two distinct independent Gaussian random variables. The model parameters are assumed to be unknown but bounded with known lower and upper bounds. We adopt J.E. Besag´s (1986) iterated conditional modes (ICM) algorithm and make it robust in a maximin sense. We have previously (1998) discussed this problem in the case of two kinds of Gaussian on different regions with the same variance and different means. In this paper, we deal with the case of this problem in which the two kinds of Gaussian on the different regions have the same mean and different variances. The most attractive properties of the derived algorithm are: (a) there is no need to go through computationally expensive parameter estimations which are usually not implementable in parallel; (b) the algorithm is fully parallel; (c) the algorithm can be implemented by recurrent neural networks