George Caridakis
Kostas Karpouzis
ABSTRACT
This work focuses on two of the research problems comprising automatic sign language recognition, namely robust computer vision techniques for consistent hand detection and tracking, while preserving the hand shape contour which is useful for extraction of features related to the handshape and a novel classification scheme incorporating Self-organizing maps, Markov chains and Hidden Markov Models. Geodesic Active Contours enhanced with skin color and motion information are employed for the hand detection and the extraction of the hand silhouette, while features extracted describe hand trajectory, region and shape. Extracted features are used as input to separate classifiers, forming a robust and adaptive architecture whose main contribution is the optimal utilization of the neighboring characteristic of the SOM during the decoding stage of the Markov chain, representing the sign class.
- M. J. Black and A. D. Jepson. Recognizing temporal trajectories using the condensation algorithm. In 3rd. international Conference on Face and Gesture Recognition, 1998. Google Scholar
Digital Library
- V. Caselles, R. Kimmel, and G. Sapiro. Geodesic Active Contours. Int'l J. Computer Vision, 22(1):61--79, 1997. Google ScholarDigital Library
- E. Efthimiou and S.-E. Fotinea. GSLC: Creation and annotation of a greek sign language corpus for hci. Universal Access in Human Computer Interaction. Coping with Diversity, pages 657--666, 2007. Google ScholarDigital Library
- G. Fang, W. Gao, and D. Zhao. Large vocabulary sign language recognition based on fuzzy decision trees. Systems, Man and Cybernetics, Part A, IEEE Transactions on, 34(3):305--314, 2004. Google ScholarDigital Library
- B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proc. of Imaging understanding workshop, pages 121--130, 1981.Google Scholar
- V.-M. Mantyla, J. Mantyjarvi, T. Seppanen, and E. Tuulari. Hand gesture recognition of a mobile device user. In IEEE International Conference on Multimedia and Expo, 2000.Google ScholarCross Ref
- S. Ong and S. Ranganath. Automatic sign language analysis: a survey and the future beyond lexical meaning. Transactions on Pattern Analysis and Machine Intelligence, 27(6):873--891, 2005. Google ScholarDigital Library
- S. Osher and J. Sethian. Fronts propagating with curvature-dependent speed:algorithms based on Hamilton-Jacobi formulations. Journ. of Comp. Physics, 79:12--49, 1988. Google ScholarDigital Library
- I. Ozer, L. Tiehan, and W. Wolf. Design of a real-time gesture recognition system: High performance through algorithms and software. Signal Processing Magazine, IEEE, 22:57--64, 2005.Google ScholarCross Ref
- N. Paragios and R. Deriche. Geodesic active regions: A new framework to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, 13(1/2):249--268, March 2002.Google ScholarDigital Library
- T. Starner, J. Weaver, and A. Pentland. Real-time american sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1371--1375, 1998. Google ScholarDigital Library
- H. Wang, M. C. Leu, and C. Oz. American sign language recognition using multi-dimensional hidden markov models. Journal of Information Science and Engineering, 22, 5:1109--1123, 2006.Google Scholar
- A. Wilson and A. Bobick. Parametric hidden markov models for gesture recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 21(9), 1999. Google ScholarDigital Library
- S. Wong and R. Cipolla. Continuous gesture recognition using a sparse bayesian classifier. In 18th international Conference on Pattern Recognition, ICPR. IEEE Computer Society, 2006. Google ScholarDigital Library
- Y. Wu and T. Huang. Hand modeling, analysis, and recognition for vision-based human computer interaction. IEEE Signal Processing Magazine, 18:51--60, 2001.Google ScholarCross Ref
- M.-H. Yang, N. Ahuja, and M. Tabb. Extraction of 2d motion trajectories and its application to hand gesture recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(8):1061--1074, 2002. Google ScholarDigital Library
Index Terms
- Automatic sign language recognition: vision based feature extraction and probabilistic recognition scheme from multiple cues
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