Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
View Invariance for Human Action Recognition
International Journal of Computer Vision
Definition and recovery of kinematic features for recognition of American sign language movements
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Exploring modeling language for multi-touch systems using petri nets
Proceedings of the 2013 ACM international conference on Interactive tabletops and surfaces
Hidden Markov model for human to computer interaction: a study on human hand gesture recognition
Artificial Intelligence Review
Max-Margin Early Event Detectors
International Journal of Computer Vision
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The main characteristics of human hand gestures can be summarized by their dynamic, multiattribute property. To utilize hand gestures as a way of interaction, it is necessary to analyze the motion patterns for each of the gesture attributes and finally to extract the whole interpretation by integrating the relevant factors across time. Previous research has shown the possibility for recognition of local aspects of hand gesture. But the global framework for finding the whole interpretation from the local aspects has yet to be provided. We propose a colored Petri net model for high-level description of hand gestures. This model intercommunicates with simultaneous low-level recognizers and thus finds a whole-interpretation for the gesture