Abstract
A novel wavelet-based joint transform correlator (WJTC) for rotation-invariant pattern recognition and applications in optical image processing and remote sensing is investigated. First an optimal set of filter parameters and a mother wavelet filter are selected. These are used to extract features at different resolution from a set of rotationally distorted training images. Then a composite reference feature is formulated from these features for use in the WJTC. Simulation results for both noisy and noiseless environments are presented to verify the effectiveness of this technique.
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