
Accession Number : ADA118866
Title : Application of the Conditional PopulationMixture Model to Image Segmentation.
Descriptive Note : Technical rept.,
Corporate Author : ILLINOIS UNIV AT CHICAGO CIRCLE DEPT OF MATHEMATICS
Personal Author(s) : Sclove,Stanley L
PDF Url : ADA118866
Report Date : 15 Aug 1982
Pagination or Media Count : 25
Abstract : The problem of image segmentation is considered in the context of a mixture of probability distributions. The segments fall into classes. A probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. A numerical example is given. Choice of the number of classes, using Akaike's information criterion (AIC) for model identification, is illustrated.
Descriptors : *Image processing, *Multivariate analysis, *Population(Mathematics), Relaxation, Segmented, Probability distribution functions, Parameters, Algorithms, Statistical analysis, Mathematical models, Matrices(Mathematics), Maximum likelihood estimation
Subject Categories : Statistics and Probability
Human Factors Engineering & Man Machine System
Distribution Statement : APPROVED FOR PUBLIC RELEASE