DYNAMIC VISION SENSOR COLOR CAMERA

    公开(公告)号:US20250126371A1

    公开(公告)日:2025-04-17

    申请号:US18953136

    申请日:2024-11-20

    Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

    Spectral imaging method and system

    公开(公告)号:US10101206B2

    公开(公告)日:2018-10-16

    申请号:US15524827

    申请日:2015-11-05

    Abstract: An imaging system and method are presents for use in reconstructing spectral data of an object. The imaging system comprises: an optical unit; a pixel array of a detector; and a data processor for receiving and processing image data indicative of light detected by the pixel array and generating reconstructed spectral data of the object being imaged. The optical unit is configured and operable for applying a predetermined coding to an input light field while creating an optical image thereof on a detection plane defined by the pixel array. Therefore, the image data is a function of the predetermined coding and a spectrum of the object to be determined.

    HIGH FREQUENCY SENSITIVE NEURAL NETWORK
    10.
    发明公开

    公开(公告)号:US20240281642A1

    公开(公告)日:2024-08-22

    申请号:US18563388

    申请日:2022-05-26

    CPC classification number: G06N3/0464

    Abstract: A computer-implemented method of extracting high-frequency features from data, including receiving a first dataset; in a training phase, applying frequency-based guidance to learnable high-frequency filters in a neural network, the frequency based-guidance including promoting high eigenvalues associated with eigenvectors comprising the learnable high-frequency filters; extracting from the first dataset high-frequency features associated with the eigenvectors; and using the trained high-frequency filters to extract high-frequency features from a second dataset.

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