Template recognition by image expansion with nonorthogonal wavelets

The authors present a novel approach to template recognition by signal expansion into a set of nonorthogonal template-similar basis functions (wavelets). It is shown that expansion matching is a special case of the general nonorthogonal expansion and is equivalent to restoration of undegraded images. Matching by expansion is quite robust in conditions of noise, superposition, and severe occultation. Expansion matching also maximizes a novel and more practically defined discriminative signal-to-noise ratio (DSNR). It is proved that maximizing the DSNR is equivalent to minimum squared error restoration by Wiener filters. Experimental comparisons with the widely used correlation matching (matched filtering) show that expansion matching yields much higher DSNR