A number of applied problems are effectively solved with simple Markov-Gibbs random field (MGRF) models of spatially homogeneous or piecewise-homogeneous images provided that their identification (parameter estimation) is able to focus such a prior on a particular class of images. We propose more accurate analytical potential estimates for a generic MGRF with multiple pairwise pixel interaction and use them for structural analysis and synthesis of stochastic and periodic image textures.
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Gimel'farb, G., Zhou, D. (2008). Texture Analysis by Accurate Identification of a Generic Markov–Gibbs Model. In: Bunke, H., Kandel, A., Last, M. (eds) Applied Pattern Recognition. Studies in Computational Intelligence, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76831-9_9
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