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Hand part classification using single depth images

Title
Hand part classification using single depth images
Authors
Sohn, Myoung-KyuKim, Dong-JuKim, Hyunduk
DGIST Authors
Sohn, Myoung-Kyu; Kim, Dong-Ju; Kim, Hyunduk
Issue Date
2015
Citation
12th Asian Conference on Computer Vision, ACCV 2014, 9009, 253-261
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
0302-9743
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. © Springer International Publishing Switzerland 2015.
URI
http://hdl.handle.net/20.500.11750/3685
DOI
10.1007/978-3-319-16631-5_19
Publisher
Springer Verlag
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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