Accession Number:

ADA159692

Title:

Optimal Bayesian Estimators for Image Segmentation and Surface Reconstruction,

Descriptive Note:

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s):

Report Date:

1985-04-01

Pagination or Media Count:

19.0

Abstract:

A very fruitful approach to the solution of image segmentation and surface reconstruction tasks is their formulation as estimation problems via the use of Markov random field models and Bayes theory. However, the Maximum a Posteriori estimate, which is the one most frequently used, is suboptimal in these cases. The author shows that for segmentation problems, the optimal Bayesian estimator is the maximizer of the posterior marginals, while for reconstruction tasks, the thresholded posterior mean has the best possible performance. He presents efficient distributed algorithms for approximating these estimates in the general case. Based on these results, developed is a maximum likelihood procedure that leads to a parameter-free distributed algorithm for restoring piecewise constant images. To illustrate these ideas, the reconstruction of binary patterns is discussed in detail. Additional keywords artificial intelligence computer vision. Author

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE