結合多感測器的手勢辨識於人機互動及智慧型服務機器人之應用

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doi:10.6342/NTU.2012.02976

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结合多感测器的手势辨识於人机互动及智慧型服务机器人之应用

Multi-Sensor Based Hand Gesture Recognition for Human-Robot Interaction and the application of Intelligent Service Robotics

吴彦彰 , 硕士　　导师：罗仁权　　

英文 DOI： 10.6342/NTU.2012.02976

智慧型服务机器人；电脑视觉；数位影像处理；机器学习；人机器互动； CAR 方程式； Intelligent Service Robot ； Computer Vision ； Digital Image Processing ； Machine Learning ； Human-Robot Interaction (HRI) ； Combinatorial Approach Recognizer ； CAR equation

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摘要 │ 参考文献 (45) │ 国际计量

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With advances in technology, robots play an important role in our lives. Nowadays, we have more chance to see robots service in our society such as intelligent robot for rescue and for service. Therefore, Human-Robot interaction becomes an essential issue for research. In this thesis, we propose a direct method for Human-Robot interaction on service robot. Thesis can be broadly divided into two parts; in the first part, we introduce a combining method for hand gesture recognition which is using computer vision and image processing to achieve the goal and second the part, we show several methods for Human-Robot interaction.
	In the first part, we focus on our hand gesture recognition method. Hand sign recognition is an essential and fast way for Human-Robot Interaction (HRI). Sign language is the most intuitive and direct way to communication for impaired or disabled people. Through the hand or body gestures, the disabled can efficiently let caregiver or robot know what message and order they want to convey. In this thesis, we propose a combinatorial hands gesture recognition algorithm which combines two distinct recognizers. These two recognizers collectively determine the hand’s gesture via a process called Combinatorial Approach Recognizer (CAR) equation. These two recognizers are aimed to complement the ability of discrimination. To achieve this goal, one recognizer recognizes hand gesture by hand skeleton recognizer (HSR), and the other recognizer is based on Support Vector Machines (SVM). In addition, the corresponding classifiers of SVM are trained using variety features such as Gabor feature, local binary pattern and raw data. Furthermore, the trained images are using Bosphorus Hand Database [15][20][43] , in addition to the images taken by us. A set of rules including recognizer switching and combinatorial approach recognizer CAR equation is devised to synthesize the distinctive methods. We have successfully demonstrated gesture recognition experimentally with successful proof of concept. Our experiment can be use in self-developed intelligent robots and to achieve Human-Robot interaction. 
	In the second, we introduce several ways of Human-Robot interactions and demonstrate the application of intelligent service robots, such as hand gesture recognition which is proposed in first part implements on service robot, RGB-D sensor application and multi-sensor based person identification. We associate different kind of sensor to achieve innovative human-computer interaction methods.

参考文献 ( 45 ) 〈TOP〉

[1] A. Barkoky and N.M. Charkari, “Static Hand Gesture Recognition of Persian Sign Numbers using Thinning Method,” ICMT 2011
连结：
[3] C. Benadelkader and P. Griffin, “A Local Region-Based Approach to Gender Classification from Face Images,” Proc. 2005 IEEE CS Conf.Computer Vision and Pattern Recognition, p. 52, 2005.
连结：
[4] C. L. Breazeal, Sociable Machines: Expressive Social Exchange Between Humans and Robots. Sc.D. dissertation, Department of Electrical Engineering and Computer Science, MIT, May, 2000
连结：
[6] L. Bretzner, I. Laptev, and T. Lindeberg, “Hand gesture recognition using multi-scale color features, hierarchical models and particle filtering,” in Proc. 5th IEEE Int. Conf. Autom. Face Gesture Recog., 2002, pp. 405–410.
连结：
[7] L. Breiman, ”Bagging predictors,” Machine Learning, 24, 123–140, 1996.
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使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。