TY - GEN
T1 - Nonlinear PHMMs for the interpretation of parameterized gesture
AU - Wilson, Andrew D.
AU - Bobick, Aaron F.
PY - 1998
Y1 - 1998
N2 - In previous work, we modify 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.
AB - In previous work, we modify 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.
UR - https://www.scopus.com/pages/publications/0032305828
U2 - 10.1109/CVPR.1998.698708
DO - 10.1109/CVPR.1998.698708
M3 - Conference contribution
AN - SCOPUS:0032305828
SN - 0818684976
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 879
EP - 884
BT - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
T2 - Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Y2 - 23 June 1998 through 25 June 1998
ER -