By Bahram Javidi and Joseph L. Horner (Eds.)
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Extra info for Real-Time Optical Information Processing
2. H. J. Caulfield and W. T. Maloney, "Improved discrimination in optical character recognition/' Appl. Opt. 8, 2354 (1969). 3. D. Casasent and D. Psaltis, "Position, rotation and scale invariant opti cal correlation," Appl. Opt. 15, 1795 (1976). 4. For a review of correlation filters, please see D. L. Flannery and J. L. Horner, "Fourier optical signal processors," Proc. IEEE 77, 1511 (1989). 38 Javidi · Refregier · Wang · Willett 5. J. L. Horner, "Metrics for assessing pattern recognition performance/' Appl.
In most situations, it is not obvious a priori what features should be used. Unfortunately, good features can be selected only by combining a healthy dose of intuition and thorough testing. Once the features are calculated from the observed image, a smaller num ber of features are usually generated using feature compression techniques. The goal of these techniques is to retain as few features as necessary while retaining the classification performance. While covariance transformation techniques such as the Karhunen-Loeve (K-L) transform, Fukunaga-Koontz mapping, and Foley-Sammon mapping are available, most often feature compression is carried out by trial-and-error methods.
Furthermore, if the input noise is overlap ping with the target, then W^co) = δ(ω) and the generalized matched filter function is simplified to H » = -f^- . 55) is the same as the conventional matched filter function ob tained by maximization of the classic definition of SNR under the condition that the target to be detected is in the presence of zero mean overlapping sta tionary noise. The conventional matched filter function in Eq. 55) is a special case of the generalized matched filter function in Eq.