Real-time hand gesture feature extraction using depth data

Hao Huang; Zhaojie Ju; Honghai Liu

doi:10.1109/ICMLC.2014.7009118

Real-time hand gesture feature extraction using depth data

Hao Huang, Zhaojie Ju, Honghai Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2014 International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	206-213
ISBN (Electronic)	978-1-4799-4215-2
ISBN (Print)	978-1-4799-4216-9, 978-1-4799-4214-5
DOIs	https://doi.org/10.1109/ICMLC.2014.7009118
Publication status	Published - 15 Jan 2015
Event	2014 International Conference on Machine Learning and Cybernetics - Lanzhou, China Duration: 13 Jul 2014 → 16 Jul 2014

Publication series

Name
ISSN (Print)	2160-133X

Conference

Conference	2014 International Conference on Machine Learning and Cybernetics
Abbreviated title	ICMLC 2014
Country/Territory	China
City	Lanzhou
Period	13/07/14 → 16/07/14

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10.1109/ICMLC.2014.7009118

！REAL-TIME HAND GESTURE FEATURE EXTRACTION USING DEPTH
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Accepted author manuscript (Post-print), 947 KB

http://ieeexplore.ieee.org/document/7009118/

Cite this

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title = "Real-time hand gesture feature extraction using depth data",

abstract = "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.",

author = "Hao Huang and Zhaojie Ju and Honghai Liu",

year = "2015",

month = jan,

day = "15",

doi = "10.1109/ICMLC.2014.7009118",

language = "English",

isbn = "978-1-4799-4216-9",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "206--213",

booktitle = "Proceedings of the 2014 International Conference on Machine Learning and Cybernetics (ICMLC)",

address = "United States",

note = "2014 International Conference on Machine Learning and Cybernetics, ICMLC 2014 ; Conference date: 13-07-2014 Through 16-07-2014",

}

Huang, H, Ju, Z & Liu, H 2015, Real-time hand gesture feature extraction using depth data. in Proceedings of the 2014 International Conference on Machine Learning and Cybernetics (ICMLC). Institute of Electrical and Electronics Engineers Inc., pp. 206-213, 2014 International Conference on Machine Learning and Cybernetics, Lanzhou, China, 13/07/14. https://doi.org/10.1109/ICMLC.2014.7009118

TY - GEN

T1 - Real-time hand gesture feature extraction using depth data

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AU - Ju, Zhaojie

AU - Liu, Honghai

PY - 2015/1/15

Y1 - 2015/1/15

N2 - 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.

AB - 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.

U2 - 10.1109/ICMLC.2014.7009118

DO - 10.1109/ICMLC.2014.7009118

M3 - Conference contribution

SN - 978-1-4799-4216-9

SN - 978-1-4799-4214-5

SP - 206

EP - 213

BT - Proceedings of the 2014 International Conference on Machine Learning and Cybernetics (ICMLC)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 International Conference on Machine Learning and Cybernetics

Y2 - 13 July 2014 through 16 July 2014

ER -

Real-time hand gesture feature extraction using depth data

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