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

Personal Author(s) : Derin,Haluk ; Elliott,Howard ; Cristi,Roberto ; Geman,Donald

PDF Url : ADA133966

Report Date : Aug 1983

Pagination or Media Count : 45

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.

Descriptors :   *Algorithms, *Bayes theorem, *Image processing, Segmented, Mathematical models, Markov processes, Recursive functions, Two dimensional, Computations

Subject Categories : Statistics and Probability
      Optics

Distribution Statement : APPROVED FOR PUBLIC RELEASE