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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 15, 1982
Accession Number
ADA118866

Entities

People

  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programs
  • Illinois
  • Image Processing
  • Image Segmentation
  • Markov Models
  • Mathematics
  • Military Research
  • Normal Distribution
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Random Variables
  • Recognition
  • Statistical Analysis
  • Statistical Inference
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Computer Vision.
  • Statistical inference.