A method of extracting improved features for object identification by correlating with a wavelet filter is described. The wavelet filter used is a linear combination of Gabor wavelets, which is designed by a neural network algorithm to extract features that are useful for discriminating different classes of objects. The neural network algorithm achieves this by iteratively adapting the filter parameters and linear combination weights of the wavelet filter so that the features extracted maximize the Fisher ratio between the classes. The algorithm thus provides an automated technique of designing a filter which extracts improved features for identification. Results are presented which show the ability of these improved features to increase the classification performance of a pattern recognition system.

Date Published: 7 June 1996
PDF: 12 pages
Proc. SPIE 2751, Hybrid Image and Signal Processing V, (7 June 1996); doi: 10.1117/12.241999

Published in SPIE Proceedings Vol. 2751:
Hybrid Image and Signal Processing V
David P. Casasent; Andrew G. Tescher, Editor(s)