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Author Wilson, A.D. ♦ Bobick, A.F.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1998
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hidden Markov models ♦ Reactive power ♦ Face recognition ♦ Laboratories ♦ Azimuth ♦ Microwave integrated circuits ♦ Testing ♦ Parameter estimation ♦ Logistics ♦ Neural networks
Abstract Recently we modified the hidden Markov model (HMM) framework to incorporate a global parametric variation in the output probabilities of the states of the HMM. Development of the parametric hidden Markov model (PHMM) was motivated by the task of simultaneously recognizing and interpreting gestures that exhibit meaningful variation. With standard HMMs, such global variation confounds the recognition process. The original PHMM approach assumes a linear dependence of output density means on the global parameter. In this paper we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. We show a generalized expectation-maximization (GEM) algorithm for training the PHMM and a GEM algorithm to simultaneously recognize the gesture and estimate the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural azimuth/elevation parameterization of pointing direction.
Description Author affiliation: Media Lab., MIT, Cambridge, MA, USA (Wilson, A.D.)
ISBN 0818684976
ISSN 10636919
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1998-06-25
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 93.56 kB
Page Count 6
Starting Page 879
Ending Page 884

Source: IEEE Xplore Digital Library