Spatial frequency feature extraction for a classification system using wavelets

摘要

An iterative process to determine the wavelet function and combination of scales of the function which provides data where there is a large separability compared to the separability of the data set prior to processing. Wavelets are selected for inclusion in a library in accordance with predetermined criteria and then applied to a digitized signal by convolution to perform digital filtering. The convolution of each wavelet is performed for the number of times dictated by the coefficients of the wavelet for each of the input signal samples. Separability of the wavelet implemented digital filtration is calculated as a percentage for each wavelet. The separation data is stored in memory until the iterative process is applied to all wavelets. The separability data is then examined to identify the wavelet producing the greatest separation. The data separability is estimated using a likelihood ratio after the probability densities for each of two sets of profile data are estimated. The lower and upper bounds for a Bayes error are determined using resubstitution (R) and leave one out (L) methods, respectively.