Title page for etd-0810117-010628


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URN etd-0810117-010628
Author Shao-yu Ouyang
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2017
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Real-Time Hand Gesture Recognition with Leap Motion
Date of Defense 2017-09-04
Page Count 75
Keyword
  • Human-Computer Interaction
  • Multinomial Logistic Regression
  • Leap Motion
  • Machine Learning
  • hand gesture recognition
  • 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.
    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
  • Files
  • etd-0810117-010628新.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2017-09-13

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