<p>Recognising and estimating gestures is a fundamental aspect towards translating from a sign language to a spoken language. It is a challenging problem and at the same time, a growing phenomenon in Computer Vision. This thesis presents two approaches, an example-based and a learning-based approach, for performing integrated detection, segmentation and 3D estimation of the human upper body from a single camera view. It investigates whether an upper body pose can be estimated from a database of exemplars with labelled poses. It also investigates whether an upper body pose can be estimated using skin feature extraction, Support Vector Machines (SVM) and a 3D human body model. The example-based and learning-based approaches obtained success rates of 64% and 88%, respectively. An analysis of the two approaches have shown that, although the learning-based system generally performs better than the example-based system, both approaches are suitable to recognise and estimate upper body poses in a South African sign language recognition and translation system.</p>
| Identifer | oai:union.ndltd.org:UNWC/oai:UWC_ETD:http%3A%2F%2Fetd.uwc.ac.za%2Findex.php%3Fmodule%3Detd%26action%3Dviewtitle%26id%3Dgen8Srv25Nme4_2493_1304504127 |
| Date | January 2011 |
| Creators | Achmed, Imran. |
| Source Sets | Univ. of Western Cape |
| Language | English |
| Detected Language | English |
| Type | Thesis and dissertation |
| Format | |
| Coverage | ZA |
| Rights | Copyright: University of the Western Cape |
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