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.
ASJC Scopus subject areas
- Environmental Science(all)
- Earth and Planetary Sciences(all)