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Tree Approximations to Markov Random Fields
April 1995 (vol. 17 no. 4)
pp. 391-402

Abstract—Methods for approximately computing the marginal probability mass functions and means of a Markov random field (MRF) by approximating the lattice by a tree are described. Applied to the a posteriori MRF these methods solve Bayesian spatial pattern classification and image restoration problems. The methods are described, several theoretical results concerning fixed-point problems are proven, and four numerical examples are presented, including comparison with optimal estimators and the Iterated Conditional Mode estimator and including two agricultural optical remote sensing problems.

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Index Terms:
Markov random field, Bayesian estimation, spatial pattern classification, image segmentation, image restoration.
Chi-hsin Wu, Peter C. Doerschuk, "Tree Approximations to Markov Random Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 4, pp. 391-402, April 1995, doi:10.1109/34.385979
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