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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

9. A Novel Approach to Extract Hand Gesture Feature in Depth Images

verfasst von : Honghai Liu, Zhaojie Ju, Xiaofei Ji, Chee Seng Chan, Mehdi Khoury

Erschienen in: Human Motion Sensing and Recognition

Verlag: Springer Berlin Heidelberg

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Abstract

This chapter proposes a novel approach to extract human hand gesture features in real-time from RGB-D images based on the earth mover’s distance and Lasso algorithms. Firstly, hand gestures with hand edge contour are segmented using a contour length information based de-noise method. A modified finger earth mover’s distance algorithm is then applied to locate the palm image and extract fingertip features. Lastly and more importantly, a Lasso algorithm is proposed to effectively and efficiently extract the fingertip feature from a hand contour curve. Experimental results are discussed to demonstrate the effectiveness of the proposed approach.

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Vorheriges Kapitel Human Hand Motion Analysis with Multisensory Information
Nächstes Kapitel Recognizing Constrained 3D Human Motion: An Inference Approach
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Metadaten
Titel
A Novel Approach to Extract Hand Gesture Feature in Depth Images
verfasst von
Honghai Liu
Zhaojie Ju
Xiaofei Ji
Chee Seng Chan
Mehdi Khoury
Copyright-Jahr
2017
Verlag
Springer Berlin Heidelberg
Buch
Human Motion Sensing and Recognition

Print ISBN: 978-3-662-53690-2

Electronic ISBN: 978-3-662-53692-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-662-53692-6_9