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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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
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