Hand part classification using single depth images

Myoung Kyu Sohn; Dong Ju Kim; Hyunduk Kim

doi:10.1007/978-3-319-16631-5_19

Hand part classification using single depth images

Myoung Kyu Sohn, Dong Ju Kim, Hyunduk Kim

Division of Automotive Technology

Daegu Gyeongbuk Institute of Science and Technology

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

1 Scopus citations

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.

Original language	English
Title of host publication	Computer Vision - ACCV 2014 Workshops, Revised Selected Papers
Editors	C.V. Jawahar, Shiguang Shan
Publisher	Springer Verlag
Pages	253-261
Number of pages	9
ISBN (Print)	9783319166308
DOIs	https://doi.org/10.1007/978-3-319-16631-5_19
State	Published - 2015
Event	12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore Duration: 1 Nov 2014 → 2 Nov 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9009
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th Asian Conference on Computer Vision, ACCV 2014
Country/Territory	Singapore
City	Singapore
Period	1/11/14 → 2/11/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Access to Document

10.1007/978-3-319-16631-5_19

Cite this

Sohn, M. K., Kim, D. J., & Kim, H. (2015). Hand part classification using single depth images. In C. V. Jawahar, & S. Shan (Eds.), Computer Vision - ACCV 2014 Workshops, Revised Selected Papers (pp. 253-261). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9009). Springer Verlag. https://doi.org/10.1007/978-3-319-16631-5_19

Sohn, Myoung Kyu ; Kim, Dong Ju ; Kim, Hyunduk. / Hand part classification using single depth images. Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. editor / C.V. Jawahar ; Shiguang Shan. Springer Verlag, 2015. pp. 253-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Sohn, MK, Kim, DJ & Kim, H 2015, Hand part classification using single depth images. in CV Jawahar & S Shan (eds), Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9009, Springer Verlag, pp. 253-261, 12th Asian Conference on Computer Vision, ACCV 2014, Singapore, Singapore, 1/11/14. https://doi.org/10.1007/978-3-319-16631-5_19

Hand part classification using single depth images. / Sohn, Myoung Kyu; Kim, Dong Ju; Kim, Hyunduk.
Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. ed. / C.V. Jawahar; Shiguang Shan. Springer Verlag, 2015. p. 253-261 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9009).

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

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T1 - Hand part classification using single depth images

AU - Sohn, Myoung Kyu

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AU - Kim, Hyunduk

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

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N2 - 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.

AB - 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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Sohn MK, Kim DJ, Kim H. Hand part classification using single depth images. In Jawahar CV, Shan S, editors, Computer Vision - ACCV 2014 Workshops, Revised Selected Papers. Springer Verlag. 2015. p. 253-261. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-16631-5_19

Hand part classification using single depth images

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