(22) Stanley L. Sclove. Application of the conditional population-mixture model to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 5 (1983), 428-433.

The problem of image segmentation is considered in the context of a mixture of probability distribution. 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.