Unsupervised Parameter Selection for Gesture Recognition with Vector Quantization and Hidden Markov Models

Abstract : This article presents an investigation of a heuristic approach for unsupervised parameter selection for gesture recognition system based on Vector Quantization (VQ) and Hidden Markov Model (HMM). The two stage algorithm which uses histograms of distance measurements is proposed and tested on a database of natural gestures recorded with motion capture glove. Presented method allows unsupervised estimation of parameters of a recognition system, given example gesture recordings, with savings in computation time and improved performance in comparison to exhaustive parameter search.
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Communication dans un congrès
Pedro Campos; Nicholas Graham; Joaquim Jorge; Nuno Nunes; Philippe Palanque; Marco Winckler. 13th International Conference on Human-Computer Interaction (INTERACT), Sep 2011, Lisbon, Portugal. Springer, Lecture Notes in Computer Science, LNCS-6949 (Part IV), pp.170-177, 2011, Human-Computer Interaction – INTERACT 2011. 〈10.1007/978-3-642-23768-3_14〉
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Przemysław Głomb, Michał Romaszewski, Arkadiusz Sochan, Sebastian Opozda. Unsupervised Parameter Selection for Gesture Recognition with Vector Quantization and Hidden Markov Models. Pedro Campos; Nicholas Graham; Joaquim Jorge; Nuno Nunes; Philippe Palanque; Marco Winckler. 13th International Conference on Human-Computer Interaction (INTERACT), Sep 2011, Lisbon, Portugal. Springer, Lecture Notes in Computer Science, LNCS-6949 (Part IV), pp.170-177, 2011, Human-Computer Interaction – INTERACT 2011. 〈10.1007/978-3-642-23768-3_14〉. 〈hal-01597029〉

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