Statistical Approaches to Texture Classification
Manik Varma · 2004
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. The thesis starts by introducing a filter bank based approach to the problem of texture classification. We design low dimensional, rotation and scale invariant filter sets which are nevertheless capable of extracting rich features at multiple orientations and scales. Textures are modelled by the frequency distribution of exemplar filter response features. Characterising a texture by multiple models allows the classification of single images without requiring any knowledge of the imaging conditions. Using this framework, it is demonstrated that the new filter sets achieve superior performance as compared