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    3D Hand Gesture Recognition Using a Depth and Skeletal Dataset

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    Date
    2017
    Author
    Smedt, Quentin De
    Wannous, Hazem
    Vandeborre, Jean-Philippe
    Guerry, J.
    Saux, B. Le
    Filliat, D.
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    Abstract
    Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging hand gesture dataset containing 14 gestures, performed by 28 participants executing the same gesture with two different numbers of fingers. Two research groups have participated to this track, the accuracy of their recognition algorithms have been evaluated and compared to three other state-of-the-art approaches.
    BibTeX
    @inproceedings {10.2312:3dor.20171049,
    booktitle = {Eurographics Workshop on 3D Object Retrieval},
    editor = {Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov},
    title = {{3D Hand Gesture Recognition Using a Depth and Skeletal Dataset}},
    author = {Smedt, Quentin De and Wannous, Hazem and Vandeborre, Jean-Philippe and Guerry, J. and Saux, B. Le and Filliat, D.},
    year = {2017},
    publisher = {The Eurographics Association},
    ISSN = {1997-0471},
    ISBN = {978-3-03868-030-7},
    DOI = {10.2312/3dor.20171049}
    }
    URI
    http://dx.doi.org/10.2312/3dor.20171049
    https://diglib.eg.org:443/handle/10.2312/3dor20171049
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    • 3DOR 17

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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA