Real-Time Optical Information Processing by Bahram Javidi and Joseph L. Horner (Eds.)

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.

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