Noisy texture classification: A higher-order statistics approach

摘要

In this paper, a novel texture classification scheme using higher-order statistics (HOS) functions as discriminating features is proposed. It is well known that such statistical parameters are insensitive to additive Gaussian noise. In particular, third-order statistical parameters, i.e. third-order cumulants and bispectrum, are insensitive to any symmetrically distributed noise, and also exhibit the capability of better characterizing non-Gaussian signals. By exploiting these HOS properties, it is possible to devise a robust method for classifying textures affected by noise with different distributions and even with very low signal-to-noise ratios.