MARKOV RANDOM FIELD MODEL WITH APPLICATIONS TO TEXTURE CLASSIFICATION AND DISCRIMINATION.

J. Zhang, J. W. Modestino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recent developments of the Markov Random Field (MRF) model, also known as the Gibbs Random Field, have shown that the MRF can provide a powerful image model for many image processing applications. In this paper the MRF model is applied to texture classification and texture discrimination problems which are formulated as statistical decision problems. Different texture classes are discriminated by using maximum-likelihood (ML) decision rules. The class-conditional likelihood functionals are derived from the conditional probability distribution function of the assumed MRF model. Results of texture classification and discrimination experiments are described.

Original languageEnglish
Title of host publicationUnknown Host Publication Title
Place of PublicationPrinceton, NJ, USA
PublisherPrinceton Univ
Pages230-237
Number of pages8
StatePublished - Dec 1 1986

ASJC Scopus subject areas

  • Engineering(all)

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