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Low‐level bayesian segmentation of piecewise‐homogeneous noisy and textured images
Author(s) -
Gimel'farb G. L.,
Zalesny A. V.
Publication year - 1991
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.1850030308
Subject(s) - maximum a posteriori estimation , piecewise , gibbs sampling , mathematics , markov random field , prior probability , segmentation , a priori and a posteriori , boosting (machine learning) , posterior probability , histogram , simulated annealing , artificial intelligence , bayesian probability , computer science , image segmentation , algorithm , statistics , image (mathematics) , mathematical analysis , maximum likelihood , philosophy , epistemology
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 marginal a posteriori probability (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 marginal a posteriori probabilities 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.