Decision rules for choice of neighbors in random field models of images

R. L. Kashyap, R. Chellappa, N. Ahuja

Research output: Contribution to journalArticlepeer-review


Random field models have many applications in image processing and analysis. The mainconcern of this paper is to design a decision rule for fitting an appropriate random field model to a given image. We assume that the given image is a particular realization of a homogenous Gaussian discrete random field. We represent the underlying random field by a set of parametric models representing the spatial dependence. Using spectral representations of the random field and standard Bayesian methods, we develop a decision rule for choosing an appropriate model from a class of such models. We discuss the relevance of the theory developed in this paper for applications in image modeling and texture characterization.

Original languageEnglish (US)
Pages (from-to)301-318
Number of pages18
JournalComputer Graphics and Image Processing
Issue number4
StatePublished - Apr 1981

ASJC Scopus subject areas

  • General Environmental Science
  • General Earth and Planetary Sciences


Dive into the research topics of 'Decision rules for choice of neighbors in random field models of images'. Together they form a unique fingerprint.

Cite this