Texture modelling and segmenting by multiple pairwise pixel interactions

Novel joint and conditional non-Markov Gibbs random field models are proposed for simulating and segmenting piecewise-uniform grayscale image textures under arbitrary linear transformations of their gray ranges. Structure of interactions is recovered using analytical initial estimates of Gibbs potentials. These estimates are refined then by a stochastic approximation. The models embed both image simulation and segmentation into the same Bayesian processing framework. Experiments with simulated and natural textures confirm an efficacy of the models