Toward a vision-based hand gesture interface
VRST '94 Proceedings of the conference on Virtual reality software and technology
Description and recognition methods for sign language based on gesture components
Proceedings of the 2nd international conference on Intelligent user interfaces
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A framework for recognizing the simultaneous aspects of American sign language
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Proceedings of the International Gesture Workshop on Gesture and Sign Language in Human-Computer Interaction
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GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
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GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Real-Time Continuous Gesture Recognition System for Sign Language
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Relevant Features for Video-Based Continuous Sign Language Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
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Large vocabulary sign language recognition based on fuzzy decision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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A dilemma, caused by data variation, exists in research into signer-independent sign language recognition. An effective way to solve this dilemma, and thereby help push the research forward, is to understand sign language from the perspectives of human kinesics and linguistics. This paper, based on the principles of movement observation science, specifically Laban Movement Analysis (LMA), presents a summary of the factors causing sign language data variation, proposes the definition of, and a method for describing, sign language effort elements, and then provides a strategy for standardizing signer-independent sign language data. The standardized data are to be used for training and recognition. The method presented in this paper has been assessed under different experimental conditions, and the results show that the recognition accuracy is greatly increased.