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
Robust Real-Time Face Detection
International Journal of Computer Vision
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Using Dynamic Programming for Appearance-Based Sign Language Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Sign Recognition using Depth Image Streams
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Hidden Conditional Random Fields for Gesture Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Large Vocabulary Continuous Sign Language Recognition Based on OP/Viterbi Algorithm
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Performance Evaluation of Stereo Algorithms for Automotive Applications
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
An evaluation framework for stereo-based driver assistance
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
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For the recognition of continuous sign language we analyse whether we can improve the results by explicitly incorporating depth information. Accurate hand tracking for sign language recognition is made difficult by abrupt and fast changes in hand position and configuration, overlapping hands, or a hand signing in front of the face. In our system depth information is extracted using a stereo-vision method that considers the time axis by using pre- and succeeding frames. We demonstrate that depth information helps to disambiguate overlapping hands and thus to improve the tracking of the hands. However, the improved tracking has little influence on the final recognition results.