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    Feature point identification in sparse optical flow based tracking in a computer vision system
    22.
    发明授权
    Feature point identification in sparse optical flow based tracking in a computer vision system 有权

    公开(公告)号：US11915431B2

    公开(公告)日：2024-02-27

    申请号：US16532658

    申请日：2019-08-06

    Applicant: TEXAS INSTRUMENTS INCORPORATED

    Inventor： Deepak Kumar Poddar ,  Anshu Jain ,  Desappan Kumar ,  Pramod Kumar Swami

    IPC: G06T7/246

    CPC classification number: G06T7/246 ,  G06T2200/28 ,  G06T2207/20016 ,  G06T2207/30241

    Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

    Efficient SIMD implementation of 3x3 non maxima suppression of sparse 2D image feature points
    26.
    发明授权
    Efficient SIMD implementation of 3x3 non maxima suppression of sparse 2D image feature points 有权

    公开(公告)号：US11010631B2

    公开(公告)日：2021-05-18

    申请号：US16730622

    申请日：2019-12-30

    Applicant: TEXAS INSTRUMENTS INCORPORATED

    Inventor： Deepak Kumar Poddar ,  Pramod Kumar Swami ,  Prashanth Viswanath

    IPC: G06K9/46 ,  G06K9/62 ,  G06K9/00

    Abstract: In accordance with disclosed embodiments, an image processing method includes performing a first scan in a first direction on a first list of pixels in which, for each pixel in the first list, a feature point property is compared with a corresponding feature point property of each of a first set of neighboring pixels, performing a second scan in a second direction on the first list of pixels in which, for each pixel in the first list, a feature point property is compared with a corresponding feature point property of each of a second set of neighboring pixels, using the results of the first and second scans to identify pixels from the first list to be suppressed, and forming a second list of pixels that includes pixels from the first list that are not identified as pixels to be suppressed. The second list represents a non-maxima suppressed list.

    Estimation of time to collision in a computer vision system
    27.
    发明授权
    Estimation of time to collision in a computer vision system 有权

    公开(公告)号：US10977502B2

    公开(公告)日：2021-04-13

    申请号：US16272415

    申请日：2019-02-11

    Applicant: Texas Instruments Incorporated

    Inventor： Prashanth Ramanathpur Viswanath ,  Deepak Kumar Poddar ,  Soyeb Nagori ,  Manu Mathew

    IPC: G06K9/00 ,  G06T7/73 ,  G06T7/246

    Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.

    Estimation of time to collision in a computer vision system
    29.
    发明授权
    Estimation of time to collision in a computer vision system 有权

    公开(公告)号：US10248872B2

    公开(公告)日：2019-04-02

    申请号：US15298218

    申请日：2016-10-19

    Applicant: Texas Instruments Incorporated

    Inventor： Prashanth Ramanathpur Viswanath ,  Deepak Kumar Poddar ,  Soyed Nagori ,  Manu Mathew

    IPC: G06K9/00 ,  H04N7/18 ,  G06T7/73 ,  G06T7/246

    Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.

    EFFICIENT SIMD IMPLEMENTATION OF 3X3 NON MAXIMA SUPPRESSION OF SPARSE 2D IMAGE FEATURE POINTS
    30.
    发明申请
    EFFICIENT SIMD IMPLEMENTATION OF 3X3 NON MAXIMA SUPPRESSION OF SPARSE 2D IMAGE FEATURE POINTS 审中-公开

    公开(公告)号：US20190005349A1

    公开(公告)日：2019-01-03

    申请号：US15989551

    申请日：2018-05-25

    Applicant: TEXAS INSTRUMENTS INCORPORATED

    Inventor： Deepak Kumar Poddar ,  Pramod Kumar Swami ,  Prasanth Viswanath

    IPC: G06K9/46 ,  G06K9/62 ,  G06K9/00

    CPC classification number: G06K9/4609 ,  G06K9/00986 ,  G06K9/4671 ,  G06K9/6202

    Abstract: This invention transforms a list of feature points in raster scan order into a list of maxima suppressed feature points. A working buffer has two more entries than the width of the original image. Each entry is assigned to an x coordinate of the original image. Each entry stores a combined y coordinate and reliability score for each feature point in the original list. This process involves a forward scan and a backward scan. For each original feature point its x coordinate defines the location within the working buffer where neighbor feature points would be stored if they exist. The working buffer initial data and the y coordinates assure a non-suppress comparison result if the potential neighbors are not actual neighbors. For actual neighbor data, the y coordinates match and the comparison result depends solely upon the relative reliability scores.

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