Towards Efficient Texture Classification and Abnormality Detection

One of the fundamental issues in image processing and machine vision is texture, specifically texture feature extraction, classification and abnormality detection. This thesis is concerned with the analysis and classification of natural and random textures, where the building elements and the structure of texture are not clearly determinable, hence statistical and signal processing approaches are more appropriate. We investigate the advantages of multi-scale/multidirectional signal processing methods, higher order statistics-based schemes, and computationally low cost texture analysis algorithms. Consequently these advantages are combined to form novel algorithms