Multiresolution GMRF models for texture segmentation

Santhana Krishnamachari, Ramalingam Chellappa

Abstract: A multiresolution model for Gauss Markov random fields (GMRF) is presented. Coarser resolution sample fields are obtained by either subsampling or local averaging the sample field at the fine resolution. Although Markovianity is lost under such resolution transformation, coarser resolution non-Markov random fields can be effectively approximated by Markov fields. We use a local conditional distribution invariance approximation, to estimate the parameters of the coarser resolution processes from the fine resolution parameters. This multiresolution model is used to perform texture segmentation.