Please use this identifier to cite or link to this item: https://doi.org/10.1109/AFGR.2004.1301588
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dc.title3-D hand trajectory recognition for signing exact english
dc.contributor.authorKong, W.W.
dc.contributor.authorRanganath, S.
dc.date.accessioned2014-06-19T02:51:58Z
dc.date.available2014-06-19T02:51:58Z
dc.date.issued2004
dc.identifier.citationKong, W.W., Ranganath, S. (2004). 3-D hand trajectory recognition for signing exact english. Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition : 535-540. ScholarBank@NUS Repository. https://doi.org/10.1109/AFGR.2004.1301588
dc.identifier.isbn0769521223
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68676
dc.description.abstractThis paper presents a hierarchical approach to recognize isolated 3-D hand gesture trajectories for Signing Exact English (SEE). SEE hand gestures can be periodic as well as non-periodic. We first differentiate between periodic and non-periodic gestures followed by recognition of individual gestures. After periodicity detection, non-periodic trajectories are classified into 8 classes and periodic trajectories are classified into 4 classes. A Polhemus tracker is used to provide the input data. Periodicity detection is based on Fourier analysis and hand trajectories are recognized by Vector Quantization Principal Component Analysis (VQPCA). The average periodicity detection accuracy is 95.9%. The average recognition rates with VQPCA for non-periodic and periodic gestures are 97.3% and 97.0% respectively. In comparison, k-means clustering yielded 87.0% and 85.1%, respectively.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/AFGR.2004.1301588
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/AFGR.2004.1301588
dc.description.sourcetitleProceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition
dc.description.page535-540
dc.identifier.isiut000222059800087
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