Optimal Bayesian Estimators for Image Segmentation and Surface Reconstruction
Author(s)
Marroquin, Jose L.
Date Issued
April 1, 1985
Series/Report no.
AIM-839
Abstract
sA very fruitful approach to the solution of image segmentation andssurface reconstruction tasks is their formulation as estimationsproblems via the use of Markov random field models and Bayes theory.sHowever, the Maximuma Posteriori (MAP) estimate, which is the one mostsfrequently used, is suboptimal in these cases. We show that forssegmentation problems the optimal Bayesian estimator is the maximizersof the posterior marginals, while for reconstruction tasks, thesthreshold posterior mean has the best possible performance. We presentsefficient distributed algorithms for approximating these estimates insthe general case. Based on these results, we develop a maximumslikelihood that leads to a parameter-free distributed algorithm forsrestoring piecewise constant images. To illustrate these ideas, thesreconstruction of binary patterns is discussed in detail.
Subjects
Bayesian estimation
Markov random fields
image segmentation
ssurface reconstruction
image restoration
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