Hand Tracking with an Extended Self-Organizing Map

  • Andreea State
  • Foti Coleca
  • Erhardt Barth
  • Thomas Martinetz
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 198)

Abstract

We introduce an extension of the self-organizing map for performing 3D hand skeleton tracking. We use a range camera for data acquisition and apply a SOM-like learning process within each frame in order to capture the hand pose. Our method uses a topology consisting of 1D and 2D segments for an improved representation of the hand. The proposed algorithm is very efficient and produces good tracking results.

Keywords

hand skeleton tracking self-organizing maps kinect 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andreea State
    • 1
    • 2
  • Foti Coleca
    • 1
    • 3
  • Erhardt Barth
    • 1
    • 3
  • Thomas Martinetz
    • 1
  1. 1.Institute for Neuro- and BioinformaticsUniversity of LübeckLübeckGermany
  2. 2.University ”POLITEHNICA” of BucureştiBucureştiRomania
  3. 3.Gestigon GmbH, Innovations Campus LübeckLübeckGermany

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