<title>Wavelet GRI-MINACE filter for rotation-invariant pattern recognition</title>

Abstract

A rotation-invariant optical correlation filter using wavelet transform to produce easily detectable correlation peaks in the presence of noise and to provide better intraclass recognition is proposed. The proposed filter is designed by using the energy spectra of the wavelet transformed reference image and random noise. Because the energy spectrum of the wavelet transformed reference image is higher than that of the random noise in the specified band, the proposed filter has good discrimination ratio (DR), high signal to noise ratio (SNR), and low distortion sensitivity (DS). The wavelet function used in this paper is the Mexican-hat function, and it is chosen by investigating the relation between the energy spectra of the reference image and the various wavelet functions. The optimal dilation parameters of the wavelet function are also achieved with varying the dilation parameters of the wavelet function.