Chapter 4 TEXEMS: Random Texture Representation and Analysis

Random textures are notoriously more difficult to deal with than regular textures particularly when detecting abnormalities on object sur-faces. In this chapter, we present a statistical model to represent and analyse random textures. In a two-layer structure a texture image, as the first layer, is considered to be a superposition of a number of texture exemplars, possibly overlapped, from the second layer. Each texture exemplar, or simply texem, is characterised by mean values and cor-responding variances. Each set of these texems may comprise various sizes from different image scales. We explore Gaussian mixture models in learning these texem representations, and show two different applica-tions: novelty detection and image segmentation. Texture is one of the most important characteristics in identifying objects and understanding surfaces. There are numerous texture features reported in the literature, with some covered elsewhere in this book, used to perfor