Bayes Smoothing Algorithms for Segmentation of Images Modelled by Markov Random Fields.

Abstract

A new image segmentation algorithm is presented, based on recursive Bayes smoothing of images modelled by Markov random fields and corrupted by independent additive noise. The Bayes smoothing algorithm yields the a posteriori distribution of the scene value at each pixel, given the total noisy image, in a recursive way. The a posteriori distribution together with a criterion of optimality then determine a Bayes estimate of the scene. Examples are given where the algorithm is applied to test imagery and also SEASAT SAR imagery.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1983
Accession Number
ADA133966

Entities

People

  • Donald Geman
  • Haluk Derin
  • Howard Elliott
  • Roberto Cristi

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Computations
  • Computer Vision
  • Image Processing
  • Image Segmentation
  • Markov Chains
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Sequences
  • Statistics
  • Synthetic Aperture Radar
  • Two Dimensional

Readers

  • Computer Vision.
  • Statistical inference.