Abstract
Present work introduces a probabilistic recognition scheme for hand gestures. Self organizing feature maps are used to model spatiotemporal information extracted through image processing. Two models are built for each gesture category and, along with appropriate distance metrics, produce a validated classification mechanism that performs consistently during experi-ments on acted gestures video sequences.
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Caridakis, G., Pateritsas, C., Drosopoulos, A., Stafylopatis, A., Kollias, S. (2007). Probabilistic Video-Based Gesture Recognition Using Self-organizing Feature Maps. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_27
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DOI: https://doi.org/10.1007/978-3-540-74695-9_27
Publisher Name: Springer, Berlin, Heidelberg
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