Robust segmentation of MRF texture images with uncertain parameters

A robust algorithm is proposed for the segmentation of a special class of texture images where 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 vanables. The model parameters are assumed unknown but bounded with known lower and upper bounds. We adopt the Besag's Iterated Conditional Modes (ICM) algorithm and make it robust in a maximin sense. In a previous report [1], we discussed this problem and developed a robust ICM segmentation algorithm. In this paper, we show that our previous ICM algorithm can be substantially simplified. We further prove a maximin theorem which is useful for the design of fixed and robust ICM segmentation algorithm. 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.