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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/65336

    Title: Real-time Hand Motion Parameter Estimation with Feature Points Detection using Kinect
    Authors: Chang, Che-Hao;Hsieh, Pei-Chi;Huang, Chung-Lin;Chang, Chun-Ming
    Contributors: 資訊多媒體應用學系
    Keywords: Hand motion parameter estimation, regres-sion function random forest, Kinect
    Date: 2013-12
    Issue Date: 2013-12-18 10:45:26 (UTC+8)
    Abstract: This paper presents a real-time Kinect-based hand pose estimation method. Different from model-based and appearance-based, our approach retrieves continuous hand motion parameters in real time. First, the hand re-gion is segmented from the depth image. Second, some specific feature points on the hand are located by random forest classifier, and the relative displacements of these feature points are converted as a rotation invariant fea-ture vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. The experimental results are compared with the ground truth obtained by data glove for reliabili-ty evaluation. The effects of different distances and dif-ferent rotation angles to the estimation accuracy are eva-luated. Finally, we ask some other subjects to evaluate the system adaptively of our system to different hand shapes.
    Relation: 2013全國計算機會議
    Appears in Collections:[行動商務與多媒體應用學系] 會議論文

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