Statistical Approaches To Texture Classification

Abstract

This thesis investigates the problem of classifying textures from their imaged appearance without imposing any constraints on, or requiring any a priori knowledge of, the viewing or illumination conditions under which the images were obtained. Classification algorithms based on the statistical distribution of texton primitives are developed to categorise single, uncalibrated images into a set of pre-learnt material classes.