Image and video for hearing impaired people
Journal on Image and Video Processing
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Effort analysis in signer-independent sign gestures
Journal of Experimental & Theoretical Artificial Intelligence
Synthetic data generation technique in Signer-independent sign language recognition
Pattern Recognition Letters
Appearance based recognition methodology for recognising fingerspelling alphabets
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Fuzzy Sets and Systems
Probabilistic video-based gesture recognition using self-organizing feature maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Energy-based blob analysis for improving precision of skin segmentation
Multimedia Tools and Applications
Hand gesture recognition based on SOM and ART
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
A two-stage visual turkish sign language recognition system based on global and local features
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Convexity local contour sequences for gesture recognition
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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The major difficulty for large vocabulary sign recognition lies in the huge search space due to a variety of recognized classes. How to reduce the recognition time without loss of accuracy is a challenging issue. In this paper, a fuzzy decision tree with heterogeneous classifiers is proposed for large vocabulary sign language recognition. As each sign feature has the different discrimination to gestures, the corresponding classifiers are presented for the hierarchical decision to sign language attributes. A one- or two- handed classifier and a hand-shaped classifier with little computational cost are first used to progressively eliminate many impossible candidates, and then, a self-organizing feature maps/hidden Markov model (SOFM/HMM) classifier in which SOFM being as an implicit different signers' feature extractor for continuous HMM, is proposed as a special component of a fuzzy decision tree to get the final results at the last nonleaf nodes that only include a few candidates. Experimental results on a large vocabulary of 5113-signs show that the proposed method dramatically reduces the recognition time by 11 times and also improves the recognition rate about 0.95% over single SOFM/HMM.