Optical classification of random image fields using spectral synthetic discriminant functions

It is shown that the problem of classification of images that have the perfectly random nature may be solved with the help of synthetic discriminant functions being synthesized by least-squares technique to separate linearly the power spectra of the corresponding random image fields. The realization of the proposed method by means of an optical technique is discussed, and its efficiency is illustrated by two examples of real-life texture classification.