Abstract
Basic features and drawbacks of habitual Markov/Gibbs image models are discussed and novel Markov and non-Markov models with multiple pairwise pixel interactions to describe uniform and piecewise-uniform grayscale textures are introduced. The models possess the similar learning (parameter estimation) schemes and allow to integrate image simulating and segmenting. Some experiments are presented.
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