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. A modification of the usual approach to mixtures of distributions is employed. Parametric families of distributions are considered, a set of parameter values being associated with each distribution. In addition, an identification parameter is associated with each observation, indicating from which distribution the observation arose. Thus, the segmentation problem is cast as a problem of statistical estimation. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 15, 1980
Accession Number
ADA088183

Entities

People

  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programs
  • Covariance
  • Data Science
  • Image Processing
  • Image Segmentation
  • Information Processing
  • Information Science
  • New York
  • Normal Distribution
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis

Fields of Study

  • Mathematics

Readers

  • Computer Vision.
  • Statistical inference.