Texture Discrimination Based Upon an Assumed Stochastic Texture Model

James W. Modestino, Acie L. Vickers, Robert W. Friles

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed.

Original languageEnglish (US)
Pages (from-to)557-580
Number of pages24
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number5
StatePublished - Sep 1981


  • Digital filtering
  • image processing
  • random fields
  • texture discrimination

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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