TEXEMS: Random Texture Representation and Analysis

Random textures are notoriously more difficult to deal with than regular textures particularly when detecting abnormalities on object surfaces. 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 corresponding 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 applications: novelty detection and image segmentation.