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2015 | OriginalPaper | Chapter

Hand Part Classification Using Single Depth Images

Authors : Myoung-Kyu Sohn, Dong-Ju Kim, Hyunduk Kim

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

Hand pose recognition has received increasing attention as an area of HCI. Recently with the spreading of many low cost 3D camera, researches for understanding more natural gestures have been studied. In this paper we present a method for hand part classification and joint estimation from a single depth image. We apply random decision forests(RDF) for hand part classification. Foreground pixels in the hand image are estimated by RDF, which is called per-pixel classification. Then hand joints are estimated based on the classified hand parts. We suggest robust feature extraction method for per-pixel classification, which enhances the accuracy of hand part classification. Depth images and label images synthesized by 3D hand mesh model are used for algorithm verification. Finally we apply our algorithm to the real depth image from conventional 3D camera and show the experiment result.

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Metadata
Title
Hand Part Classification Using Single Depth Images
Authors
Myoung-Kyu Sohn
Dong-Ju Kim
Hyunduk Kim
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16630-8

Electronic ISBN: 978-3-319-16631-5

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16631-5_19

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