Texture analysis and synthesis using a generic Markov-Gibbs image model

Abstract

Texture analysis and synthesis have become very important research subjects in digital image processing and computer vision. The primary focus of this thesis is on developing a structural identification of a generic Markov-Gibbs random field model of textures which results in a new method for fast texture analysis and synthesis. Probability models, in particular Markov-Gibbs random fields (MGRF) have gained wide acceptance for solving applied image recognition, analysis, and synthesis problems. An MGRF model of textures is usually specified by a Gibbs probability distribution on selected texture features such as image signal statistics. Despite of modelling power and expressiveness of an MGRF model, the traditional identification of the model, i.e. estimating model parameters for a particular image, is a computationally complex process, because it usually involves Markov Chain Monte Carlo (MCMC) algorithms with an exponential time complexity.