Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction

Simone Ciotti; Edoardo Battaglia; Iason Oikonomidis; Alexandros Makris; Aggeliki Tsoli; Antonio Bicchi; Antonis Argyros; Matteo Bianchi

Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction

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@INPROCEEDINGS{10.1007/978-3-319-58877-3_42,
    author={Simone Ciotti and Edoardo Battaglia and Iason Oikonomidis and Alexandros Makris and Aggeliki Tsoli and Antonio Bicchi and Antonis Argyros and Matteo Bianchi},
    title={Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction},
    proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
    proceedings_a={MOBIHEALTH},
    year={2017},
    month={6},
    keywords={},
    doi={10.1007/978-3-319-58877-3_42}
}

Simone Ciotti
Edoardo Battaglia
Iason Oikonomidis
Alexandros Makris
Aggeliki Tsoli
Antonio Bicchi
Antonis Argyros
Matteo Bianchi
Year: 2017
Synergy-Driven Performance Enhancement of Vision-Based 3D Hand Pose Reconstruction
MOBIHEALTH
Springer
DOI: 10.1007/978-3-319-58877-3_42

Simone Ciotti^,*, Edoardo Battaglia¹^,*, Iason Oikonomidis²^,*, Alexandros Makris²^,*, Aggeliki Tsoli²^,*, Antonio Bicchi^,*, Antonis Argyros²^,*, Matteo Bianchi¹^,*

1: University of Pisa
2: Foundation for Research and Technology, Hellas

*Contact email: simone.ciotti@centropiaggio.unipi.it, e.battaglia@centropiaggio.unipi.it, oikonom@ics.forth.gr, amakris@ics.forth.gr, aggeliki@ics.forth.gr, bicchi@centropiaggio.unipi.it, argyros@ics.forth.gr, matteo.bianchi@centropiaggio.unipi.it

Abstract

In this work we propose, for the first time, to improve the performance of a Hand Pose Reconstruction (HPR) technique from RGBD camera data, which is affected by self-occlusions, leveraging upon , i.e., information on how human most commonly use and shape their hands in everyday life tasks. More specifically, in our approach, we ignore joint angle values estimated with low confidence through a vision-based HPR technique and fuse synergistic information with such incomplete measures. Preliminary experiments are reported showing the effectiveness of the proposed integration.

Published: 2017-06-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-58877-3_42