Abstract
This chapter reviews the Markov Mesh models as originally given in the works of Abend, Harley, and Kanal. It also presents some inputs on some related references and developments of Markov Random Fields (MRF) models. The Markov Mesh models presented in the works of these authors sought to incorporate spatial dependence in reducing the complexity of likelihood functions for image classification. Current attempts to use MRF as a model of textured digital images may have a better chance of producing useful results. The Markov Mesh model is more useful for the generation of images than in the estimation of image parameters for the classification of real images, for which other simpler procedures seem to work equally well or better.
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