Journal
IET IMAGE PROCESSING
Volume 7, Issue 1, Pages 70-80Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-ipr.2012.0273
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Funding
- National Science Foundation of China [61005018, 91120005]
- Programme for New Century Excellent Talents in University [NCET-10-0079]
- NPU Foundation for Fundamental Research [NPU-FFR-JC20120237]
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This study addresses the problem of vision-based sign language recognition, which is to translate signs to English. The authors propose a fully automatic system that starts with breaking up signs into manageable subunits. A variety of spatiotemporal descriptors are extracted to form a feature vector for each subunit. Based on the obtained features, subunits are clustered to yield codebooks. A boosting algorithm is then applied to learn a subset of weak classifiers representing discriminative combinations of features and subunits, and to combine them into a strong classifier for each sign. A joint learning strategy is also adopted to share subunits across sign classes, which leads to a more efficient classification. Experimental results on real-world hand gesture videos demonstrate the proposed approach is promising to build an effective and scalable system.
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