The statistics of gray-level differences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level differences and their multidimensional distributions for texture description. The present approach has important advantages compared to earlier related approaches based on gray level cooccurrence matrices or histograms of absolute gray-level differences. Experiments with difficult texture classification and supervised texture segmentation problems show that our approach provides a very good and robust performance in comparison with the mainstream paradigms such as cooccurrence matrices, Gaussian Markov random fields, or Gabor filtering. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 19]
展开▼
