Segmentation of Digital Images Using Spatial and Contextual Information via a Two-Dimensional Markov Model

摘要

The problem of partitioning a digital image into segments is considered. First the procedure is illustrated for the analogous one-dimensional problem, namely, segmentation of time series. Then similar ideas are applied to the segmentation of digital images. The segments are considered as falling 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. The label process is modeled as a Markov process. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood (a relaxation method) to the resulting likelihood function. In this paper special attention is given to the situation in which the observations are conditionally independent, given the labels. Numerical examples are given. Choice of the number of classes, using statistical model-selection criteria, is illustrated. (Author)