Probability models have gained wide acceptance, because of their modelling power and expressiveness. These models pose the problem of texture analysis into a statistical setting, which allows a wide range of well-established theories and methodologies in mathematical statistics to be introduced into texture modelling. In particular, Markov-Gibbs random fields (MGRF), which describe a texture in terms of spatial geometry and quantitative strengths of inter-pixel statistical dependency, are among the most successful probability models.

A probability model is usually specified by a parametric probability distribution. The model is to be `identified', in order to find best values for unknown parameters of the model for a given training texture. Due to usually complex mathematical form of the distribution, model identification is not trivial. For instance, identification of a generic MGRF model involves a process of stochastic approximation having exponential time complexity [37].

This thesis considers a more efficient identification of a generic MGRF model that characterises a texture by a group of texels and placement rules of their spatial arrangement. Combining the strength of model-based analysis and structural approach, the proposed method results in a novel texel-based texture description and leads to a technique for fast texture synthesis.