| URN |
etd-0810117-010628 |
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| Author |
Shao-yu Ouyang |
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| Author's Email Address |
No Public. |
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| Statistics |
This thesis had been viewed 5355 times. Download 0 times. |
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| Department |
Computer Science and Engineering |
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| Year |
2017 |
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| Semester |
1 |
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| Degree |
Master |
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| Type of Document |
|
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| Language |
zh-TW.Big5 Chinese |
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| Title |
Real-Time Hand Gesture Recognition with Leap Motion |
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| Date of Defense |
2017-09-04 |
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| Page Count |
75 |
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| Keyword |
Human-Computer Interaction
Multinomial Logistic Regression
Leap Motion
Machine Learning
hand gesture recognition
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| Abstract |
In recent years, more and more attention has been paid to Human-Computer Interaction issues, many related studies have been published. Among the issues, Gesture Interaction is one of the most popular studies; it is almost become a trend to use gesture control technique to replace the keyboard and mouse. This thesis proposes a hand gesture recognition system, using Leap Motion Controller as a sensor, capture the features of hands and calculate the data through the Multinomial Logistic Regression algorithm in order to get the Prediction Model to classify gestures into ten kinds of gestures.
The method we propose has average recognition rate of 98%. Moreover, with the benefit of low complexity of the machine learning method we use, our system not only has the real-time performance but also is possible to run in the embedded systems. In addition, our system can also be used for the purpose of virtual keyboard or mouse, hand rehabilitation and other way to make users have a better experience. |
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| Advisory Committee |
Shi-Huang Chen - chair
Yun-Nan Chang - co-chair
Jiunn-Ru Lai - co-chair
Wei-Kuang Lai - co-chair
Chun-Hung Lin - advisor
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| Files |
Indicate in-campus at 5 year and off-campus access at 5 year. |
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| Date of Submission |
2017-09-13 |
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