A Vision-Based Approach For Unsupervised Modeling Of Signs Embedded In Continuous Sentences
The common practice in sign language recognition is to first construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use them to recognize signs in continuous sentences. In this thesis we...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
Scholar Commons
2005
|
| Subjects: | |
| Online Access: | https://scholarcommons.usf.edu/etd/788 https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1787&context=etd |