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.
