Partly-Hidden Markov model and its application to gesture recognition

Tetsunori Kobayashi, Satoshi Haruyama

Research output: Contribution to journalConference article

37 Citations (Scopus)

Abstract

A new pattern matching method, Partly-Hidden Markov model, is proposed for gesture recognition. Hidden Markov Model, which is widely used for the time series pattern recognition, can deal with only piecewise stationary stochastic process. We solved this problem by introducing the modified second order Markov Model, in which the first state is hidden and the second one is observable. As the results of 6 sign-language recognition test, the error rate was improved by 73% compared with normal HMM.

Original languageEnglish
Pages (from-to)3081-3084
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
Publication statusPublished - 1997 Jan 1
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: 1997 Apr 211997 Apr 24

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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