Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for recognizing the simultaneous aspects of American sign language
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Vision-Based Gesture Recognition: A Review
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
DigitEyes: Vision-Based Human Hand Tracking
DigitEyes: Vision-Based Human Hand Tracking
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In this work a method for metadata extraction from sign language videos is proposed, by employing high level domain knowledge. The metadata concern the depicted objects of the head and the right/left hand and the occlusion events, which are essential for interpretation and therefore for subsequent higher level semantic indexing. The occlusions between hands, head and hands and body and hands, can easily confuse metadata extraction and can consequently lead to wrong gesture interpretation. Therefore, a Bayesian network is employed to bridge the gap between the high level knowledge about the valid spatiotemporal configurations of the human body and the metadata extractor. The approach is applied here in sign-language videos, but it can be generalized to video indexing based on gestures.