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Application of the Conditional Population-Mixture Model to Image Segmentation

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1 Author(s)
Sclove, Stanley L. ; Department of Quantitative Methods, University of Illinois at Chicago, Chicago, IL 60680.

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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:PAMI-5 ,  Issue: 4 )

Date of Publication:

July 1983

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