STRUCTURED LIGHT PROJECTOR
    103.
    发明申请

    公开(公告)号:US20190249984A1

    公开(公告)日:2019-08-15

    申请号:US16275479

    申请日:2019-02-14

    Abstract: Devices and methods for 3D sensing are provided. The device comprising an optical device configured and operable to produce at least one structured light pattern, the optical device comprising a light source unit configured and operable to generate one or more light beams along predetermined one or more optical paths, and a diffractive optical unit accommodated in said one or more optical paths, at the output of said light source unit such that the diffractive optical unit faces the light source unit, the diffractive optical unit being non-periodic and two-dimensional and comprising at least one resonance-domain diffractive optical element configured to create 2D spatially variable pattern in a predetermined operative wavelength range, the diffractive optical unit being thereby configured and operable as a beam shaper for said one or more light beams to thereby create said at least one structured light pattern.

    MULTI WAVELENGTH MULTIPLEXING FOR QUANTITATIVE INTERFEROMETRY

    公开(公告)号:US20190162520A1

    公开(公告)日:2019-05-30

    申请号:US16204579

    申请日:2018-11-29

    Abstract: The present invention relates to a new interferometric module which may be implemented as holographic/interferometric portable optical setups that are based on the interferometry with multiple wavelengths acquired simultaneously, without changing the imaging parameters, such as the magnification and the resolution, and recording the quantitative complex wave front (i.e. amplitude and phase) imaging in several wavelengths simultaneously. This may be used for multiple-wavelength phase unwrapping, for recording higher or optically thicker samples or for spectroscopic holography, without loss of camera frame rate due to wavelength scanning.

    Cascaded convolutional neural network

    公开(公告)号:US10296815B2

    公开(公告)日:2019-05-21

    申请号:US15749693

    申请日:2017-04-20

    Abstract: A convolutional neural network system for detecting at least one object in at least one image. The system includes a plurality of object detectors, corresponding to a predetermined image window size in the at least one image. Each object detector is associated with a respective down-sampling ratio with respect to the at least one image. Each object detector includes a respective convolutional neural network and an object classifier coupled with the convolutional neural network. The respective convolutional neural network includes a plurality of convolution layers. The object classifier classifies objects in the image according to the results from the convolutional neural network. Object detectors associated with the same respective down-sampling ratio define at least one group of object detectors. Object detectors in a group of object detectors being associated with common convolution layers.

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