The statistics of gray-level di!erences have been successfully used in a number of texture analysis studies. In this paper we propose to use signed gray-level di!erences 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 di!erences. Experiments with di$cult texture classi"cation 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 "elds, or Gabor "ltering. ( 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
['Timo Ojala', 'Kimmo Valkealahti', 'Erkki Oja', 'Matti Pietikäinen']
Texture discrimination with multidimensional distributions of signed gray-level differences