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Reports Year : 1997

Gibbs Fields with Multiple Pairwise Pixel Interactions for Texture Simulation and Segmentation

Abstract

Modelling of spatially homogeneous and piecewise-homogeneous image textures by novel Markov and non-Markov Gibbs random fields with multiple pairwise pixel interactions is briefly overviewed. These models allow for learning both the structure and strengths (Gibbs potentials) of the interactions from a given training sample. The learning is based on first analytic and then stochastic approximation of the maximum likelihood estimates (MLE) of the potentials. A novel learning approach, giving explicit, to scaling factors, estimates of the potentials, is outlined. It exploits the conditional MLE provided that the training sample may rank a feasible top place within the parent population in its total Gibbs energy. The models embed both simulation and segmentation of the grayscale piecewise-homogeneous textures into the same Bayesian framework exploiting a controllable simulated annealing to generate the desired texture or its region map. Experimental results in simulating and segmenting various natural textures are presented and discussed.

Domains

Other [cs.OH]
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Dates and versions

inria-00073487 , version 1 (24-05-2006)

Identifiers

  • HAL Id : inria-00073487 , version 1

Cite

Georgy L. Gimel'Farb. Gibbs Fields with Multiple Pairwise Pixel Interactions for Texture Simulation and Segmentation. RR-3202, INRIA. 1997. ⟨inria-00073487⟩
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