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Walter, Michael; Psarrou, Alexandra; Gong, Shaogang
Publisher: IEEE Computer Society
Languages: English
Types: Part of book or chapter of book
Subjects: UOW3
We present an approach to automatically segment and label\ud a continuous observation sequence of hand gestures for\ud a complete unsupervised model acquisition. The method\ud is based on the assumption that gestures can be viewed\ud as repetitive sequences of atomic components, similar to\ud phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard EM approach while the determination of the number of components is based on an information criteria, the Minimum Description Length.\ud
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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