Motion analysis of grammatical processes in a visual-gestural language
Proc. of the ACM SIGGRAPH/SIGART interdisciplinary workshop on Motion: representation and perception
Fundamentals of speech recognition
Fundamentals of speech recognition
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Temporal Classification of Natural Gesture and Application to Video Coding
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Learning visual behavior for gesture analysis
ISCV '95 Proceedings of the International Symposium on Computer Vision
Understanding people pointing: the Perseus system
ISCV '95 Proceedings of the International Symposium on Computer Vision
Recognition and Interpretation of Parametric Gesture
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Sympathetic interfaces: using a plush toy to direct synthetic characters
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
The catchment feature model: a device for multimodal fusion and a bridge between signal and sense
EURASIP Journal on Applied Signal Processing
Subject-independent natural action recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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In previous work [14], 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.