Random field identification from a sample: Experimental results

Abstract In this report, we consider the problem of identifying a random field belonging to a given class, given sample generation by that random field. We take the field to be from one of two special classes: stationary fields of independent samples and fields that are simple stationary Markov chains. Interval estimators for the parameters of the field are derived from the joint frequencies of occurrence of elements of the sample. We use Monte Carlo simulations to evaluate the performance of these estimators and to investigate the tightness of some theoretical bounds for their confidence levels. We also demonstrate how these methods can be applied to the problem of texture classification or segmentation, and present examples of textures distinguishable using these methods but not distinguishable to the eye.