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Nonlinearity optimization in nonlinear joint transform correlators.

Nonlinearity optimization in nonlinear joint transform correlators. Research Abstract Details 

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  • Nonlinearity optimization in nonlinear joint transform correlators. Abstract Text:

    l p yaroslavskyL P Yaroslavsky,e maromE Marom,

    Three types of nonlinear transformations of the joint spectrum in nonlinear joint transform correlators (NLJTC's) are investigated with the purpose of achieving the highest discrimination capability in target location in a cluttered background: logarithmic transformation and the (1/k)th law transformation in combination with the limitation of the signal dynamic range and binarization by thresholding. By computer simulation carried out on a set of test images, it is shown that application of these transformations in NLJTC's may considerably improve the correlator's capacity to locate and recognize properly small objects on a cluttered background, provided there is proper selection of nonlinearity parameters. It is also shown that a moderate blur of the joint spectrum in such NLJTC's before nonlinear transformation is permissible, which simplifies the requirements of correlator optical alignment, the resolution power of correlator electronic components, or both.

    Nonlinearity optimization in nonlinear joint transform correlators. Publishing Authors By Initials

    lp yaroslavskyLP Yaroslavsky,e maromE Marom,

    For similar abstracts research abstracts see: abstracts research

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    Nonlinearity optimization in nonlinear joint transform correlators. Journal Published:

    PUBLICATION TYPE: Journal Article

    Journal: Applied optics

    VOLUME: 36

    Page Numbers: 4816-22

    Journal Abbreviation:

    ISSN: 0003-6935

    DAY: 10

    MONTH: Jul

    YEAR: 1997

    Nonlinearity optimization in nonlinear joint transform correlators. Information

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    LANGUAGE: eng

    NlmUniqueID: 247660

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    Country: United States

    United States Research PublicationUnited States Research Publication

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    MEDLINETA: Appl Opt

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