Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
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Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
GSLC: creation and annotation of a Greek sign language corpus for HCI
UAHCI'07 Proceedings of the 4th international conference on Universal access in human computer interaction: coping with diversity
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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Influence of handshape information on automatic sign language recognition
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
Design and evaluation of classifier for identifying sign language videos in video sharing sites
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Identifying Sign Language Videos in Video Sharing Sites
ACM Transactions on Accessible Computing (TACCESS)
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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.