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Updated: Jun 7, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Computer-generated multiple-object discriminant correlation filters: design by simulated annealing.

M Taniguchi, K Matsuoka, Y Ichioka

    Applied Optics
    |November 2, 2010
    PubMed
    Summary
    This summary is machine-generated.

    Quantization of computer-generated correlation filters can lead to errors. Synthesizing filters using simulated annealing and Lohmann holograms reduces quantization levels, improving pattern recognition accuracy.

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    Area of Science:

    • Computer vision
    • Optical information processing
    • Pattern recognition

    Background:

    • Computer generation of multiple-object discriminant correlation filters is crucial for pattern recognition.
    • Quantization of filter functions significantly impacts correlation response, potentially causing misdetection and misclassification.
    • This issue is particularly severe for multiple-object discriminant filters.

    Purpose of the Study:

    • To investigate the impact of quantization on computer-generated correlation filters.
    • To propose a novel method for synthesizing matched-filter functions to mitigate quantization errors.
    • To evaluate the effectiveness of the proposed method using computer simulations.

    Main Methods:

    • Synthesizing matched-filter functions using the simulated-annealing algorithm.
    • Encoding filter functions using Lohmann-type computer-generated holograms.
    • Reducing quantization levels for amplitude and phase in filter functions.

    Main Results:

    • The simulated-annealing algorithm was employed for filter synthesis.
    • Lohmann-type computer-generated holograms were utilized for encoding.
    • Computer simulations demonstrated the expected correlation responses with reduced quantization levels.

    Conclusions:

    • The proposed method effectively reduces quantization levels in amplitude and phase.
    • Synthesizing filters via simulated annealing and Lohmann holograms improves correlation response accuracy.
    • This approach offers a viable solution for reliable multiple-object discriminant correlation filtering.