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

页码：206 / 213

页数：8

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