Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Maintaining knowledge about temporal intervals
Communications of the ACM
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multiphase Learning for an Interval-Based Hybrid Dynamical System
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Dynamic Bayesian networks for audio-visual speech recognition
EURASIP Journal on Applied Signal Processing
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Facial expression representation based on timing structures in faces
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
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Modeling and describing temporal structure in multimedia signals, which are captured simultaneously by multiple sensors, is important for realizing human machine interaction and motion generation. This paper proposes a method for modeling temporal structure in multimedia signals based on temporal intervals of primitive signal patterns. Using temporal difference between beginning points and the difference between ending points of the intervals, we can explicitly express timing structure; that is, synchronization and mutual dependency among media signals. We applied the model to video signal generation from an audio signal to verify the effectiveness