By Bahram Javidi and Joseph L. Horner (Eds.)
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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. Opt. 31, 165 (1992). 6. Β. V. K. Kumar and L. Hasserbrook, "Performance measures for correla tion filters," Appl Opt. 29, 2997 (1990). 7. Ph. Refregier, "Filter design for optical pattern recognition: multicriteria optimization approach," Opt.
It is shown that for a noise-free target, the actual scene noise statistics become irrelevant to the detection process. In this case, the optimum receiver is similar to a correlator normalized by the input scene energy within the target window. The second approach is based on Wiener filtering. An MMSE filter is pre sented for pattern recognition problems with input scene noise that is spatially 32 Javidi · Refregier · Wang · Willett disjoint with the target. The filter is designed to have an output that is a delta function located at the position of the target.
44). 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.