A novel matching technique using image expansion into nonorthogonal wavelets

A novel approach for template matching by signal expansion into a set of nonorthogonal template similar basis functions (wavelets) is presented. It is shown that expansion matching is a special case of the general nonorthogonal expansion and is equivalent to restoration of undergraded 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 correlation matching (matched filtering) show that expansion matching yields much higher DSNR