Image expansion by non-orthogonal wavelets for optimal template matching

Presents a novel approach for 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 new 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