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    Large scale image recognition using global signatures and local feature information
    61.
    发明授权
    Large scale image recognition using global signatures and local feature information 有权

    公开(公告)号：US10796196B2

    公开(公告)日：2020-10-06

    申请号：US15063209

    申请日：2016-03-07

    Applicant: Nant Holdings IP, LLC

    Inventor： Bing Song ,  Liwen Lin

    IPC: G06F16/00 ,  G06K9/62 ,  G06K9/46

    Abstract: Techniques are provided that include receiving one or more global signatures for a query image in response to an image recognition query, wherein some of the plurality of global signatures are generated using local descriptors corresponding to different cropped versions of the image. A ranking order is determined for a plurality of document images based on nearest neighbor relations between document signatures corresponding to the plurality of document images and each one of the one or more global signatures for the query image. A subset of the plurality of document images is selected based on the determined ranking order. Additional document data corresponding to the selected subset of the plurality of document images is obtained, and a search result is generated based on a geometric verification between the additional document data corresponding to the selected subset of the plurality of document images and the query image.

    MULTIPARTY OBJECT RECOGNITION
    62.
    发明申请
    MULTIPARTY OBJECT RECOGNITION 审中-公开

    公开(公告)号：US20200301502A1

    公开(公告)日：2020-09-24

    申请号：US16899518

    申请日：2020-06-11

    Applicant: Nant Holdings IP, LLC

    Inventor： Bing Song ,  John Wiacek ,  David McKinnon ,  Matheen Siddiqui

    IPC: G06F3/01 ,  H04L29/06 ,  G06F3/0484 ,  G06F3/0481 ,  A63F13/65 ,  A63F13/213 ,  G06T19/00 ,  A63F13/215

    Abstract: Multiparty object recognition systems and methods are disclosed. A method of interactively manipulating virtual object data, wherein an object database is configured to store first party object data that corresponds to a first real-world object and is further configured to store second party object data that corresponds to a second real-world object, includes obtaining the first party object data and the second party object data for storage within the object database. Access to the object database is controlled such that the first party object data and the second party object data is accessible to the first party and the second party. Modification of the first party object data by the second party is facilitated to generate modified first party object data that is in accordance with at least one context parameter of the second party object data, and the modified first party object data is communicated to the first party.

    IMAGE-BASED FEATURE DETECTION USING EDGE VECTORS
    63.
    发明申请
    IMAGE-BASED FEATURE DETECTION USING EDGE VECTORS 审中-公开

    公开(公告)号：US20200293812A1

    公开(公告)日：2020-09-17

    申请号：US16888501

    申请日：2020-05-29

    Applicant: Nant Holdings IP, LLC

    Inventor： Bing Song ,  Nicholas J. Witchey

    IPC: G06K9/48 ,  G06T3/40 ,  G06F16/51 ,  G06F16/583 ,  G06K9/46 ,  G06K9/62 ,  G06T7/13 ,  G06T7/143 ,  G06T7/181 ,  G06T7/246 ,  G06K9/00 ,  G06T5/40

    Abstract: Techniques are provided in which a plurality of edges are detected within a digital image. An anchor point located along an edge of the plurality of edges is selected. An analysis grid associated with the anchor point is generated, the analysis grid including a plurality of cells. An anchor point normal vector comprising a normal vector of the edge at the anchor point is calculated. Edge pixel normal vectors comprising normal vectors of the edge at locations along the edge within the cells of the analysis grid are calculated. A histogram of similarity is generated for each of one or more cells of the analysis grid, each histogram of similarity being based on a similarity measure between each of the edge pixel normal vectors within a cell and the anchor point normal vector, and a descriptor is generated for the analysis grid based on the histograms of similarity.

    Image-based feature detection using edge vectors
    64.
    发明授权
    Image-based feature detection using edge vectors 有权

    公开(公告)号：US10679093B2

    公开(公告)日：2020-06-09

    申请号：US16297557

    申请日：2019-03-08

    Applicant: Nant Holdings IP, LLC

    Inventor： Bing Song ,  Nicholas J. Witchey

    IPC: G06K9/00 ,  G06K9/48 ,  G06T3/40 ,  G06F16/51 ,  G06F16/583 ,  G06K9/46 ,  G06K9/62 ,  G06T7/13 ,  G06T7/143 ,  G06T7/181 ,  G06T7/246 ,  G06T5/40

    Abstract: Techniques are provided in which a plurality of edges are detected within a digital image. An anchor point located along an edge of the plurality of edges is selected. An analysis grid associated with the anchor point is generated, the analysis grid including a plurality of cells. An anchor point normal vector comprising a normal vector of the edge at the anchor point is calculated. Edge pixel normal vectors comprising normal vectors of the edge at locations along the edge within the cells of the analysis grid are calculated. A histogram of similarity is generated for each of one or more cells of the analysis grid, each histogram of similarity being based on a similarity measure between each of the edge pixel normal vectors within a cell and the anchor point normal vector, and a descriptor is generated for the analysis grid based on the histograms of similarity.

    ROBUST FEATURE IDENTIFICATION FOR IMAGE-BASED OBJECT RECOGNITION
    66.
    发明申请
    ROBUST FEATURE IDENTIFICATION FOR IMAGE-BASED OBJECT RECOGNITION 审中-公开

    公开(公告)号：US20190318195A1

    公开(公告)日：2019-10-17

    申请号：US16450876

    申请日：2019-06-24

    Applicant: Nant Holdings IP, LLC

    Inventor： Bing Song ,  Liwen Lin ,  Mustafa Jaber

    IPC: G06K9/46 ,  G06K9/52 ,  G06K9/62 ,  G06T3/00 ,  G06T3/40

    Abstract: Techniques are provided that include identifying robust features within a training image. Training features are generated by applying a feature detection algorithm to the training image, each training feature having a training feature location within the training image. At least a portion of the training image is transformed into a transformed image in accordance with a predefined image transformation. Transform features are generated by applying the feature detection algorithm to the transformed image, each transform feature having a transform feature location within the transformed image. The training feature locations of the training features are mapped to corresponding training feature transformed locations within the transformed image in accordance with the predefined image transformation, and a robust feature set is compiled by selecting robust features, wherein each robust feature represents a training feature having a training feature transformed location proximal to a transform feature location of one of the transform features.

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