Visual Discrimination of Stochastic Texture Fields

Abstract

Earlier experiments by Julesz et al. on visual discrimination are reviewed and extended in the context of texture analysis of natural images. Stochastic methods are developed for the generation of pairs of synthetic texture fields possessing multiple gray levels, significant spatial correlation, and joint moments or probability densities of controllable form. Results of several visual discrimination experiments with these computer generated texture fields are then presented. It is demonstrated, for the stochastic models investigated, that humans cannot effortlessly discriminate between pairs of spatially correlated texture fields with differing third-order probability densities when their lower order densities are pairwise equal. This is a further verification of Julesz's conjecture. Also, it is shown that human observers are sensitive to relatively small changes in the spatial autocorrelation function. Examples are presented of discriminable texture fields with identical means, variances, autocorrelation functions, and third-order nearest neighbor moments.