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    Methods and apparatus to match images using semantic features
    2.
    发明授权
    Methods and apparatus to match images using semantic features 有权

    公开(公告)号：US11341736B2

    公开(公告)日：2022-05-24

    申请号：US16768559

    申请日：2018-03-01

    Applicant: Intel Corporation

    Inventor： Yimin Zhang ,  Haibing Ren ,  Wei Hu ,  Ping Guo

    IPC: G06V10/75 ,  G06K9/62 ,  G06V10/20 ,  G06V10/46

    Abstract: Methods and apparatus to match images using semantic features are disclosed. An example apparatus includes a semantic labeler to determine a semantic label for each of a first set of points of a first image and each of a second set of points of a second image; a binary robust independent element features (BRIEF) determiner to determine semantic BRIEF descriptors for a first subset of the first set of points and a second subset of the second set of points based on the semantic labels; and a point matcher to match first points of the first subset of points to second points of the second subset of points based on the semantic BRIEF descriptors.

    Estimation of human orientation in images using depth information from a depth camera
    3.
    发明授权
    Estimation of human orientation in images using depth information from a depth camera 有权

    公开(公告)号：US11164327B2

    公开(公告)日：2021-11-02

    申请号：US16098649

    申请日：2016-06-02

    Applicant: INTEL CORPORATION

    Inventor： Haibing Ren ,  Yimin Zhang ,  Xiaobo Hu ,  Fei Duan

    IPC: G06T7/00 ,  G06T7/73 ,  G06K9/00 ,  G06K9/62 ,  G06T7/50

    Abstract: Techniques are provided for estimation of human orientation and facial pose, in images that include depth information. A methodology embodying the techniques includes detecting a human in an image generated by a depth camera and estimating an orientation category associated with the detected human. The estimation is based on application of a random forest classifier, with leaf node template matching, to the image. The orientation category defines a range of angular offsets relative to an angle corresponding to the human facing the depth camera. The method also includes performing a three dimensional (3D) facial pose estimation of the detected human, based on detected facial landmarks, in response to a determination that the estimated orientation category includes the angle corresponding to the human facing the depth camera.

    Face detection window refinement using depth
    4.
    发明授权
    Face detection window refinement using depth 有权

    公开(公告)号：US10685214B2

    公开(公告)日：2020-06-16

    申请号：US15755467

    申请日：2015-09-25

    Applicant: INTEL CORPORATION

    Inventor： Haibing Ren ,  Yimin Zhang ,  Sirui Yang ,  Wei Hu

    IPC: G06T7/80 ,  G06K9/00 ,  G06T7/194 ,  G06T7/11 ,  G06K9/62

    Abstract: The present disclosure is directed to face detection window refinement using depth. Existing face detection systems may perform face detection by analyzing portions of visual data such as an image, video, etc. identified by sub-windows. These sub-windows are now determined only based on pixels, and thus may number in the millions. Consistent with the present disclosure, at least depth data may be utilized to refine the size and appropriateness of sub-windows that identify portions of the visual data to analyze during face detection, which may substantially reduce the number of sub-windows to be analyzed, the total data processing burden, etc. For example, at least one device may comprise user interface circuitry including capture circuitry to capture both visual data and depth data. Face detection circuitry in the at least one device may refine face detection by determining criteria for configuring the sub-windows that will be used in face detection.

    Detection of humans in images using depth information
    8.
    发明授权
    Detection of humans in images using depth information 有权

    公开(公告)号：US10740912B2

    公开(公告)日：2020-08-11

    申请号：US16094997

    申请日：2016-05-19

    Applicant: INTEL CORPORATION

    Inventor： Haibing Ren ,  Yimin Zhang ,  Fei Duan

    IPC: G06T7/50 ,  G06K9/00 ,  G06T7/10 ,  G06K9/62

    Abstract: Techniques are provided for detection of humans in images that include depth information. A methodology embodying the techniques includes segmenting an image into multiple windows and estimating the distance to a subject in each window based on depth pixel values in that window, and filtering to reject windows with sizes that are outside of a desired window size range. The desired window size range is based on the estimated subject distance and the focal length of the depth camera that produced the image. The method further includes generating classifier features for each remaining windows (post-filtering) for use by a cascade classifier. The cascade classifier creates candidate windows for further consideration based on a preliminary detection of a human in any of the remaining windows. The method further includes merging neighboring candidate windows and executing a linear classifier on the merged candidate windows to verify the detection of a human.

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