Decision Rules for Choice of Neighbors in Random Field Models of Images.

Abstract

Consider the parametric representations of a homogeneous (not necessarily isotropic) random field by a set of neighborhood models. Using Bayesian methods and spectral representation of random fields, this paper develops decision rules for choosing a model from a set of r such random field models which differ in their neighborhoods. The theory developed here will find applications in image modeling and texture characterization. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1979
Accession Number
ADA078369

Entities

People

  • N. Ahuja
  • R. Chellappa
  • R. L. Kashyap

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Computer Science
  • Computers
  • Data Science
  • Electrical Engineering
  • Image Processing
  • Image Restoration
  • Information Processing
  • Information Science
  • Maryland
  • Pattern Recognition
  • Probability
  • Probability Density Functions
  • Recognition
  • Scientific Research
  • Statistical Inference
  • Universities

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms