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

Georgy L. Gimel'Farb 1
1 PASTIS - Scene Analysis and Symbolic Image Processing
CRISAM - Inria Sophia Antipolis - Méditerranée
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.
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Submitted on : Wednesday, May 24, 2006 - 1:00:01 PM
Last modification on : Friday, February 4, 2022 - 3:15:37 AM
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  • HAL Id : inria-00073487, version 1



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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