A fast technique for automatic segmentation and classification of textured images

Abstract

A fast computationally efficient method for automatic segmentation and classification of textured images is presented. The method does not necessarily need a-priori information about the textures present in the image, thus avoiding the necessity of a training set of textures. A fast adaptive multichannel technique for autoregressive image model parameter estimation with fast tracking capabilities and a powerful statistical distance measure are appropriately interweaved to form the proposed technique. Specific properties of the estimation part of the algorithm are exploited to reduce greatly the computational complexity of the distance measure. Some interesting extensions of the method are discussed and examples are given which illustrate the performance of the algorithm. >