Accession Number:

ADA133966

Title:

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

Descriptive Note:

Final technical rept.,

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Report Date:

1983-08-01

Pagination or Media Count:

45.0

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.

Subject Categories:

  • Statistics and Probability
  • Optics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE