Image and video for hearing impaired people
Journal on Image and Video Processing
Smoothed Disparity Maps for Continuous American Sign Language Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
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Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Human behavior analysis from video data using bag-of-gestures
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Robust hand tracking by integrating appearance, location and depth cues
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Tracking benchmark databases for video-based sign language recognition
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Multi-modal social signal analysis for predicting agreement in conversation settings
Proceedings of the 15th ACM on International conference on multimodal interaction
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The major challenges that sign language recognition (SLR) now faces are developing methods that solve large-vocabulary continuous sign problems. In this paper, transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR. For tackling mass transition movements arisen from a large vocabulary size, a temporal clustering algorithm improved from k-means by using dynamic time warping as its distance measure is proposed to dynamically cluster them; then, an iterative segmentation algorithm for automatically segmenting transition parts from continuous sentences and training these TMMs through a bootstrap process is presented. The clustered TMMs due to their excellent generalization are very suitable for large-vocabulary continuous SLR. Lastly, TMMs together with sign models are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language. Experiments demonstrate that continuous SLR based on TMMs has good performance over a large vocabulary of 5113 Chinese signs and obtains an average accuracy of 91.9%