REAL-TIME HAND GESTURE FEATURE EXTRACTION USING DEPTH DATA

被引：0
|
作者：
Huang, Hao [1 ]
Ju, Zhaojie [2 ]
Liu, Honghai [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan, Peoples R China
[2] Univ Portsmouth, Sch Comp, Portsmouth PO1 2UP, Hants, England
来源：
PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1 | 2014年
关键词：
Hand gestures; Feature extraction; Kinect sensor; EMD;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel method is proposed to extract hand gesture features in real-time from RGB-D images captured by the Microsoft's Kinect. A contour length information based de-noise method is introduced for the hand gesture smooth segmentation and edge contour extraction. In addition, a finger earth mover's distance algorithm is applied with a novel approach to locate the palm image and extract fingertip features. Especially the proposed Lasso algorithm can effectively extract the fingertip feature from a hand contour curve correctly with excellent real-time performance.
引用
收藏
页码：206 / 213
页数：8
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