title: Application of the conditional population-mixture model to image segmentation.
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.
authors.affiliation: Department of Quantitative Methods, University of Illinois at Chicago, Chicago, IL 60680.
authors.forename: S L
authors.initials: SL
authors.lastname: Sclove