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11 August 1995 Texture analysis using genetic algorithms and partially ordered Markov models
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When using approaches for solving imaging problems such as maximum likelihood or a Bayesian decision rule, massive amounts of data are involved. In order to make the implementation on computers attainable and not overly CPU-intensive, approximations to optimal solutions are often chosen, or nonoptimal solutions sought. In this paper we present a novel solution to the problem of solving for a maximum a posteriori estimator that uses genetic algorithms to search the solution space and a new statistical model called partially ordered Markov models (POMMs). We apply the procedure to the problem of parameter fitting to stochastic models for texture. POMMs are a subclass of Markov random fields that have been shown to offer computational advantages over general Markov rnadom fields. POMMs are based on partial orderings of the lattice array. Among other properties, these models have an exact closed-form joint distribution. We show that POMMs can be used successfully for parameter fitting to texture data. A genetic algorithm is used for approximation of a solution of the maximum likelihood estimator. We also show simulated textures representing samples of the solutions found.
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Jennifer L. Davidson, Xia Hua, and Dan Ashlock "Texture analysis using genetic algorithms and partially ordered Markov models", Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995);

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