TEXTURE MODELING USING GIBBS DISTRIBUTIONS

We regard texture as a realization of a stochastic process defined on the square lattice. The model chosen is a Markov random field, which incorporates both local and global interactions, and it is a modification of the autobinomial model introduced by Besag and used by Cross and Jain for texture generation and synthesis. The problem of estimating the model parameters from a sample of independent realizations of the process is studied. A new method of estimation, bivariate pseudolikelihood, is proposed. Although computationally intense, this method is easy to implement. Consistency of the estimators is investigated and experiments are performed to assess the accuracy of the estimates. In addition, the estimated parameters are used to generate images that are visually compared to those arising from the original model. © 1992.