Gesture recognition using Hidden Markov models
69 p.
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2010
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sg-ntu-dr.10356-391202023-07-04T16:03:24Z Gesture recognition using Hidden Markov models Shubhashree Sangameswaran. Chan Kap Luk School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 69 p. In this project the aim is to develop a system to recognize a set of gestures using Hidden Markov models. The gestures chosen are 5 movements from Tai Chi. The system can serve as a platform for a complete, real time, interactive gesture recognition system with feedback. Tai Chi is a popular martial arts form in China. Features are extracted for a set of chosen moves, known as the training set and Hidden Markov models are trained for these gestures. Once training is complete, the model is tested for accuracy and robustness using a new set of input feature vectors, known as the test set. Master of Science (Signal Processing) 2010-05-21T04:44:28Z 2010-05-21T04:44:28Z 2006 2006 Thesis http://hdl.handle.net/10356/39120 application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering Shubhashree Sangameswaran. Gesture recognition using Hidden Markov models |
description |
69 p. |
author2 |
Chan Kap Luk |
author_facet |
Chan Kap Luk Shubhashree Sangameswaran. |
format |
Theses and Dissertations |
author |
Shubhashree Sangameswaran. |
author_sort |
Shubhashree Sangameswaran. |
title |
Gesture recognition using Hidden Markov models |
title_short |
Gesture recognition using Hidden Markov models |
title_full |
Gesture recognition using Hidden Markov models |
title_fullStr |
Gesture recognition using Hidden Markov models |
title_full_unstemmed |
Gesture recognition using Hidden Markov models |
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gesture recognition using hidden markov models |
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2010 |
url |
http://hdl.handle.net/10356/39120 |
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1772828613368872960 |