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A Direct Method for 3D Hand Pose Recovery
Umeå universitet, Institutionen för tillämpad fysik och elektronik. (Digital Media Lab)
Linnaeus University, Faculty of Technology, Department of Media Technology. KTH Royal Institue of Technology, Department of Media Technology and Interaction Design, School of Computer Science and Communication.ORCID iD: 0000-0003-2203-5805
Umeå universitet, Institutionen för datavetenskap. (Robotics)
Umeå universitet, Institutionen för tillämpad fysik och elektronik. (Digital Media Lab)
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2014 (English)In: Pattern Recognition (ICPR), 2014 22nd International Conference on, IEEE Press, 2014, p. 345-350Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel approach for performing intuitive 3D gesture-based interaction using depth data acquired by Kinect. Unlike current depth-based systems that focus only on classical gesture recognition problem, we also consider 3D gesture pose estimation for creating immersive gestural interaction. In this paper, we formulate gesture-based interaction system as a combination of two separate problems, gesture recognition and gesture pose estimation. We focus on the second problem and propose a direct method for recovering hand motion parameters. Based on the range images, a new version of optical flow constraint equation is derived, which can be utilized to directly estimate 3D hand motion without any need of imposing other constraints. Our experiments illustrate that the proposed approach performs properly in real-time with high accuracy. As a proof of concept, we demonstrate the system performance in 3D object manipulation. This application is intended to explore the system capabilities in real-time biomedical applications. Eventually, system usability test is conducted to evaluate the learn ability, user experience and interaction quality in 3D interaction in comparison to 2D touch-screen interaction.

Place, publisher, year, edition, pages
IEEE Press, 2014. p. 345-350
National Category
Signal Processing
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-40981DOI: 10.1109/ICPR.2014.68OAI: oai:DiVA.org:lnu-40981DiVA, id: diva2:796201
Conference
22nd International Conference on Pattern Recognition (ICPR2014)Stockholm, August 2014
Available from: 2014-04-11 Created: 2015-03-18 Last updated: 2017-04-19Bibliographically approved

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Yousefi, Shahrouz

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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