Optimal Bayesian Estimators for Image Segmentation and Surface Reconstruction,

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)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1985
Accession Number
ADA159692

Entities

People

  • J. L. Marroquin

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Programs
  • Computer Vision
  • Contracts
  • Equations
  • Estimators
  • Image Segmentation
  • Massachusetts
  • Military Research
  • Models
  • Optimal Estimators
  • Pattern Recognition
  • Probabilistic Models
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Image Processing and Computer Vision.
  • Operations Research
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms