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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Updated: Dec 4, 2025

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients
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Learnable Descriptors for Visual Search.

Andrea Migliorati, Attilio Fiandrotti, Gianluca Francini

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 23, 2020
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    Summary
    This summary is machine-generated.

    This study introduces LDVS, a novel learnable binary local descriptor for image matching. LDVS offers efficient, accurate performance in the MPEG CDVS framework, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Local feature descriptors are crucial for image matching tasks.
    • Existing methods like SIFT have limitations, especially in compressed domains.
    • The MPEG CDVS framework requires efficient and accurate descriptors.

    Purpose of the Study:

    • To propose LDVS, a learnable binary local descriptor for natural image matching.
    • To enable efficient sign-quantization and Hamming distance comparison.
    • To develop a descriptor suitable for mobile device operations.

    Main Methods:

    • A convolutional neural network architecture is employed to learn the LDVS descriptor.
    • Descriptors are optimized for sign-quantization and Hamming distance comparison.
    • A pairwise image matching pipeline is constructed within the CDVS framework.

    Main Results:

    • LDVS demonstrates superior performance compared to other learned binary descriptors in patch matching.
    • The proposed LDVS descriptors outperform compressed CDVS SIFT-like descriptors in pairwise image matching.
    • The convolutional architecture is parameter-efficient, suitable for mobile applications.

    Conclusions:

    • LDVS is an effective learnable binary local descriptor for image matching.
    • It offers a competitive alternative to traditional descriptors within the CDVS framework.
    • The descriptor's efficiency makes it suitable for resource-constrained environments.