Texture discrimination with multidimensional distributions of signed gray-level differences

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 a difficult texture classification problem show that our approach provides a very good and robust classification performance in comparison to the mainstream paradigms such as cooccurrence matrices, Gaussian Markov Random Fields, or Gabor filtering.