Texture analysis and simulations using Markov random field models

Authors:
S. Zimeras
Affiliations:
University of the Aegean, Department of Statistics and Actuarial-Financial Mathematics, Karlovassi, Samos, Greece
Venue:
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Year:
2006

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Abstract

The main goal of texture analysis is to extract useful textural information from an image. The use of Bayesian methods is an approach, which seeks to provide a unified framework in modeling within many different image processes. In this work, spatial behavior of the auto-logistic model in a rectangular lattice would be investigated, concentrating the first-order neighborhood structures. A simple deterministic model based on a univariate iterative scheme is studied which predicts the properties of these models and realizations have been generating using the Gibbs sampler to illustrate the properties. For well defined regions in the parameter space this iterative scheme is unstable leading to catastrophic and 2-cycle behavior.