METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR PROVIDING HAND SEGMENTATION FOR GESTURE ANALYSIS
    4.
    发明申请
    METHOD, APPARATUS AND COMPUTER PROGRAM PRODUCT FOR PROVIDING HAND SEGMENTATION FOR GESTURE ANALYSIS 审中-公开
    方法,装置和计算机程序产品,用于提供手段分析手段分析

    公开(公告)号:WO2010076622A1

    公开(公告)日:2010-07-08

    申请号:PCT/IB2009/007751

    申请日:2009-12-14

    CPC classification number: G06K9/00389

    Abstract: A method for providing hand segmentation for gesture analysis may include determining a target region based at least in part on depth range data corresponding to an intensity image. The intensity image may include data descriptive of a hand. The method may further include determining a point of interest of a hand portion of the target region, determining a shape corresponding to a palm region of the hand, and removing a selected portion of the target region to identify a portion of the target region corresponding to the hand. An apparatus and computer program product corresponding to the method are also provided.

    Abstract translation: 用于提供用于手势分析的手分割的方法可以包括至少部分地基于对应于强度图像的深度范围数据来确定目标区域。 强度图像可以包括描述手的数据。 该方法还可以包括确定目标区域的手部分的兴趣点,确定与手掌区域相对应的形状,以及移除目标区域的选定部分,以识别对应于该区域的目标区域的一部分 手。 还提供了一种与该方法对应的装置和计算机程序产品。

    METHODS AND APPARATUS FOR TEAM CLASSIFICATION IN SPORTS ANALYSIS

    公开(公告)号:WO2023044663A1

    公开(公告)日:2023-03-30

    申请号:PCT/CN2021/119876

    申请日:2021-09-23

    Abstract: An example apparatus includes processor circuitry to extract features from image data obtained from a plurality of cameras, the extraction of features performed using a plurality of sequential neural network layers; in response to each of the plurality of sequential neural network layer extracting the features, identify the extracted features in a torso region of the image data via a plurality of attention modules; estimate body landmarks from image data to localize an area; generate an upper heatmap mask based on a geometric center of the image data; calculate a loss function for the image data based on a cross-entropy loss, a pixel-wise loss, and a triplet loss determined from the extracted features and the generated heatmap mask; select lowest correlated classes based on calculated correlations between pairs of a plurality of classes; and calculate voting scores for groups associated with the lowest correlated classes.

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