2008 |
George Caridakis, Olga Diamanti, Kostas Karpouzis, Petros Maragos Automatic sign language recognition Conference Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08, 2008, ISBN: 9781605580678. Abstract | BibTeX | Links: [PDF] @conference{208, title = {Automatic sign language recognition}, author = { George Caridakis and Olga Diamanti and Kostas Karpouzis and Petros Maragos}, url = {http://portal.acm.org/citation.cfm?doid=1389586.1389687}, doi = {10.1145/1389586.1389687}, isbn = {9781605580678}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments - PETRA '08}, pages = {1}, 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.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } 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. |
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