Directional interpolation and cross-band filtering for hyperspectral imaging

    公开(公告)号:US10337923B2

    公开(公告)日:2019-07-02

    申请号:US15703947

    申请日:2017-09-13

    Abstract: Systems and methods are disclosed for processing spectral imaging (SI) data. A training operation estimates reconstruction matrices based on a spectral mosaic of an SI sensor, generates directionally interpolated maximum a-priori (MAP) estimations of image data based on the estimated reconstruction matrices. The training operation may determine filter coefficients for each of a number of cross-band interpolation filters based at least in part on the MAP estimations, and may determine edge classification factors based at least in part on the determined filter coefficients. The training operation may configure a cross-band interpolation circuit based at least in part on the determined filter coefficients and the determined edge classification factors. The configured cross-band interpolation circuit captures mosaic data using the SI sensor, and recovers full-resolution spectral data from the captured mosaic data.

    REGISTRATION OF RANGE IMAGES USING VIRTUAL GIMBAL INFORMATION

    公开(公告)号:US20180308249A1

    公开(公告)日:2018-10-25

    申请号:US15493859

    申请日:2017-04-21

    CPC classification number: G06T7/73 G06K9/4604 G06T7/33 G06T2207/10028

    Abstract: Systems and methods configured to generate virtual gimbal information for range images produced from 3D depth scans are described. In operation according to embodiments, known and advantageous spatial geometries of features of a scanned volume are exploited to generate virtual gimbal information for a pose. The virtual gimbal information of embodiments may be used to align a range image of the pose with one or more other range images for the scanned volume, such as for combining the range images for use in indoor mapping, gesture recognition, object scanning, etc. Implementations of range image registration using virtual gimbal information provide a realtime one shot direct pose estimator by detecting and estimating the normal vectors for surfaces of features between successive scans which effectively imparts a coordinate system for each scan with an orthogonal set of gimbal axes and defines the relative camera attitude.

    Hardware-friendly model-based filtering system for image restoration

    公开(公告)号:US10750135B2

    公开(公告)日:2020-08-18

    申请号:US16165881

    申请日:2018-10-19

    Abstract: A device (e.g., an image sensor, camera, etc.) may identify a camera lens and color filter array (CFA) sensor used to capture an image, and may determine filter parameters (e.g., a convolutional operator) based on the identified camera lens and CFA sensor. For example, a set of kernels (e.g., including a set of horizontal filters and a set of vertical filters) may be determined based on properties of a given lens and/or q-channel CFA sensor. Each kernel or filter may correspond to a row of a convolutional operator (e.g., of a restoration bit matrix) used by an image signal processor (ISP) of the device for non-linear filtering of the captured image. The corresponding outputs from the horizontal and vertical filters (e.g., two outputs of the horizontal and vertical filters corresponding to an input channel associated with the CFA sensor) may then be combined using a non-linear classification operation.

    REAL-TIME STEREO CALIBRATION BY DIRECT DISPARITY MINIMIZATION AND KEYPOINT ACCUMULATION

    公开(公告)号:US20200077073A1

    公开(公告)日:2020-03-05

    申请号:US16114748

    申请日:2018-08-28

    Abstract: A stereoscopic imaging device is configured to capture multiple corresponding images of objects from a first camera and a second camera. The stereoscopic imaging device can determine multiple sets of keypoint matches based on the multiple corresponding images of objects, and can accumulate the keypoints. In some examples, the stereoscopic imaging device can determine a vertical disparity between the first camera and the second camera based on the multiple sets of keypoint matches. In some examples, the stereoscopic imaging device can determine yaw errors between the first camera and the second camera based on the sets of keypoint matches, and can determine a yaw disparity between the first camera and the second camera based on the determined yaw errors. The stereoscopic imaging device can generate calibration data to calibrate one or more of the first camera and the second camera based on the determined vertical disparity and/or yaw disparity.

    Refinement of structured light depth maps using RGB color data

    公开(公告)号:US10445861B2

    公开(公告)日:2019-10-15

    申请号:US15432783

    申请日:2017-02-14

    Abstract: Systems and method for refining a depth map of a scene based upon a captured image of the scene. A captured depth map of the scene may contain outage areas or other areas of low confidence. The depth map may be aligned with a color image of the scene, and the depth values of the depth map may be adjusted based upon corresponding color values of the color image. An amount of refinement for each depth value of the aligned depth map is based upon the confidence value of the depth value and a smoothing function based upon a corresponding location of the depth value on the color image.

    Adjustable receiver exposure times for active depth sensing systems

    公开(公告)号:US10812731B2

    公开(公告)日:2020-10-20

    申请号:US16108882

    申请日:2018-08-22

    Abstract: Aspects of the present disclosure relate to active depth sensing. An example device may include a memory and a processor coupled to the memory. The processor may be configured to determine an amount of ambient light for a scene to be captured by a structured light receiver and adjust an exposure time for frame capture of a sensor of the structured light receiver based on the determined amount of ambient light. The exposure time of the sensor of the structured light receiver is inversely related to the determined amount of ambient light. The processor further may be configured to receive a plurality of captured frames from the structured light receiver based on the adjusted exposure time and generate an aggregated frame, including aggregating values across the plurality of captured frames.

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