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Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed...
An adaptive, invariant to user performance fluctuation or noisy input signal, gesture recognition scheme is presented based on Self Organizing Maps, Markov Models and Levenshtein sequence distance. Multiple modalities, all based on the hand position during gesturing, train different classifiers which are then fused in a weak classifier boosting-like setup by weight assignment to each stream. The adaptability...
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