Low‐level bayesian segmentation of piecewise‐homogeneous noisy and textured images

Abstract
We present a novel approach to image segmentation, differing from the known “simulated annealing” method in the following ways: the compound Bayesian decision rule and consequent maximal marginala posterioriprobability (MMAP) estimates of desired region labels in pixels; the two‐ or three‐level piecewise‐homogeneous Gibbs random field with constant control parameters as the probabilistic model of the images and region maps (in the general case such a model integrates the submodels of the region map, of the ideal intensities within each region, and of the noise distorting the ideal intensities); the stochastic relaxation with the constant control parameters of the Gibbs probability distribution only as a tool to obtain the samples of this field and estimate the unknown marginala posterioriprobabilities of the region labels by collecting in each pixel the histogram of labels for these samples; the like stochastic relaxation with directed variation of the control parameters of the Gibbs probability distribution as a tool to find maximal likelihood estimates of the unknown these parameters. Some experimental results are presented.

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