0000000000918817
AUTHOR
Ido Raveh
Invariant pattern recognition based on 1-D Wavelet functions and the polynomial decomposition
Abstract A new filter, consisting of 1-D Wavelet functions is suggested for achieving optical invariant pattern recognition. The formed filter is actually a real function, hence, it is theoretically possible to be implemented under both spatially coherent and spatially incoherent illuminations. The filter is based on the polynomial expansion, and is constructed out of a scaled bank of filters multiplied by 1-D Wavelet weight functions. The obtained output is shown to be invariant to 2-D scaling even when different scaling factors are applied on the different axes. The computer simulations and the experimental results demonstrate the potential hidden in this technique.
Single-output color pattern recognition using a fractional correlator
A novel method for performing color image pattern recogni- tion using a fractional correlator (FC) is proposed. The input plane is illuminated with three different coherent sources of wavelengths corre- sponding to RGB (red, green, and blue) colors. The output plane pro- vides a single output peak, which is a result of an incoherent addition between the three correlations obtained per each color. By using the fractional correlator, which is a partially space variant correlator, we achieve space-variance-controlled color pattern recognition. The use of the three-color illumination can drastically increase the discrimination ability of the suggested correlator. © 1997 Society of Photo-Optical…