NOISY TEXTURE CLASSIFICATION - A HIGHER-ORDER STATISTICS APPROACH

In this paper, a novel texture classification scheme using higher-orde r statistics (HOS) functions as discriminating features is proposed. I t is well known that such statistical parameters are insensitive to ad ditive Gaussian noise. In particular, third-order statistical paramete rs, i.e. third-order cumulants and bispectrum, are insensitive to any symmetrically distributed noise, and also exhibit the capability of be tter characterizing non-Gaussian signals. By exploiting these HOS prop erties, it is possible to devise a robust method for classifying textu res affected by noise with different distributions and even with very low signal-to-noise ratios. (C) 1998 Pattern Recognition Society. Publ ished by Elsevier Science Ltd. All rights reserved.