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

✍ Scribed by R.L. Kashyap; R. Chellappa; N. Ahuja

• Publisher: Elsevier Science
• Year: 1981
• Weight: 821 KB
• Volume: 15
• Category: Article
• ISSN: 0146-664X
• DOI: 10.1016/s0146-664x(81)80014-7

✦ Synopsis

Random field models have many applications in image processing and analysis. The main concern 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 re.libation 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.