Generation and parameter estimation of Markov random field textures and a parallel network for texture generation

In this thesis, a special class of Markov random fields (MRF), which is defined on two dimensional pixel arrays and represented by a few numbers called the MRF parameters, is studied as a texture model. Specifically, the generation of sample MRF textures and estimation of MRF texture parameters are considered. For the generation of sample MRF textures, an algorithm that can be implemented in a parallel manner is developed together with a parallel network which implements the algorithm. A mathematical description of the algorithm, based on finite state Markov chains is given and the structure of the network is explained. For the estimation of MRF texture parameters, a method based on histogramming of a sample MRF texture is studied cind a mathematical justification of the. method is given. Generation and parameter estimation methods studied in this thesis are tested by some computer programs and the results are observed to be satisfactory for many purposes.