Recent development in vision and image understanding related study reveals that a signal decomposition before processing may provide enormous useful information about the signal. Various signal decomposition models, such as the Gabor and wavelet transforms have been proposed. While the Gabor signal expansion creates a fixed resolution space-frequency signal representation, the wavelet transform provides a multi-resolution signal space-scale decomposition. Digital implementation of these transforms are computationally intensive both because of the nature of the coordinate-doubling of the transforms and due to the large quantity of convolution/correlation operations to be performed. Optics with its inherent parallel processing capability has been applied to many useful linear signal and image transformations for feature analysis and extraction. This paper is intended to study the suitability of using optical processing techniques for the signal Gabor and wavelet analysis. Gabor and wavelet transforms of both one- and two- dimensional signals and images are discussed. System parameters and limitation are analyzed. Preliminary experimental results are presented.
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