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Hand Tracking with an Extended Self-Organizing Map

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Book cover Advances in Self-Organizing Maps

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Correspondence to Andreea State .

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© 2013 Springer-Verlag Berlin Heidelberg

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State, A., Coleca, F., Barth, E., Martinetz, T. (2013). Hand Tracking with an Extended Self-Organizing Map. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_12

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  • DOI: https://doi.org/10.1007/978-3-642-35230-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35229-4

  • Online ISBN: 978-3-642-35230-0

  • eBook Packages: EngineeringEngineering (R0)

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