Wavelet Demodulation Codes, Statistical Independence, and Pattern Recognition

Samples from stochastic signals with suucient complexity need reveal only a little unexpected agreement, in order to reject the hypothesis that they are independent. The mere failure of a test of statistical independence can thereby serve as a basis for recognizing patterns conndently, provided they possess enough degrees-of-freedom. This paper discusses exploitation of this statistical principle in combination with wavelet image coding to extract phase descriptions of patterns. Demodulation and coarse quantization of the phase information creates decision environments characterized by well separated binomial-class distributions , and this lends itself to rapid and reliable pattern recognition.