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

Przemysław Głomb 1 Michał Romaszewski 1 Arkadiusz Sochan 1 Sebastian Opozda 1
1 IITiS - Institute of Theoretical and Applied Informatics (Polish Academy of Sciences)
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.
Keywords : Gesture recognition Vector Quantization Hidden Markov Model automatic parameter selection
Type de document :
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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