Issue No. 04 - April (1995 vol. 17)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.385979
<p><it>Abstract</it>—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.</p>
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 & Machine Intelligence, vol. 17, no. , pp. 391-402, April 1995, doi:10.1109/34.385979