Abstract
The Wiener filter, which has been used extensively for image restoration and signal processing, is employed for robust optical pattern recognition and classification. The Wiener filter is formulated to incorporate the in-class image and the out-of-class noise image into a single step filter construction. It is compared with the classical matched filter (CMF) and phase-only filter (POF), demonstrating a superior discrimination capability. The Wiener filter is incorporated into a synthetic discriminant function (SDF); correlation results show that it is tolerant to image distortion. With a 30 degree out-of-plane rotation between training set images, the Wiener filter-SDF achieves a 100% success rate in discriminating one-class of images from another. The CMF-SDF and POF-SDF fail to achieve 100% discrimination even at rotation increments of 15 degrees.
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