Accurate Identification of a Markov-Gibbs Model for Texture Synthesis by Bunch Sampling

摘要

A prior probability model is adapted to a class of images by identification, or parameter estimation from training data. We propose a new and accurate analytical identification of a generic Markov-Gibbs random field (MGRF) model with multiple pairwise interaction and use it for structural analysis and synthesis of textures.