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Inferring hand pose: A comparative study of visual shape features
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-0448-3786
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-5750-9655
2013 (English)In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013, IEEE , 2013, 6553698- p.Conference paper, Published paper (Refereed)
Abstract [en]

Hand pose estimation from video is essential for a number of applications such as automatic sign language recognition and robot learning from demonstration. However, hand pose estimation is made difficult by the high degree of articulation of the hand; a realistic hand model is described with at least 35 dimensions, which means that it can assume a wide variety of poses, and there is a very high degree of self occlusion for most poses. Furthermore, different parts of the hand display very similar visual appearance; it is difficult to tell fingers apart in video. These properties of hands put hard requirements on visual features used for hand pose estimation and tracking. In this paper, we evaluate three different state-of-the-art visual shape descriptors, which are commonly used for hand and human body pose estimation. We study the nature of the mappings from the hand pose space to the feature spaces spanned by the visual descriptors, in terms of the smoothness, discriminability, and generativity of the pose-feature mappings, as well as their robustness to noise in terms of these properties. Based on this, we give recommendations on in which types of applications each visual shape descriptor is suitable.

Place, publisher, year, edition, pages
IEEE , 2013. 6553698- p.
Keyword [en]
Comparative studies, Discriminability, Hand pose estimations, Robot learning from demonstrations, Robustness to noise, Shape descriptors, Sign Language recognition, Visual appearance, Gesture recognition
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-133862DOI: 10.1109/FG.2013.6553698Scopus ID: 2-s2.0-84881537213ISBN: 978-146735545-2 (print)OAI: oai:DiVA.org:kth-133862DiVA: diva2:663553
Conference
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013; Shanghai; China; 22 April 2013 through 26 April 2013
Note

QC 20131112

Available from: 2013-11-12 Created: 2013-11-11 Last updated: 2013-11-12Bibliographically approved

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Kjellström, Hedvig

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Thippur, AkshayaEk, Carl HenrikKjellström, Hedvig
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