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標題: 應用類神經模糊網路辨識人體手勢
Application Of Neural Fuzzy Network To Human Gesture Recognition
作者: 曾明凱
Tseng, Ming-Kai
關鍵字: Neural Fuzzy Network;類神經模糊網路;Recognition;辨識
出版社: 電機工程學系所
引用: [1] R. C. Gonzalez and R. E. Woods, Digital Image Processing 2/e, Prentice Hall, 2008 [2] H.D. Cheng, X.H. Jiang, Y. Sun and J. Wang, “Color image segmentation: advances and prospects”, Pattern Recognition, vol. 34, pp. 2259-2281, 2001. [3] Q. Zhou and J.K. Aggarwal, “Tracking and classifying moving objects from video,”Proc. IEEE Int. Workshop Performance Evaluation of Tracking and Surveillance, pp. 52-59, Dec. 2001. [4] T. C. Chen, Vision-based Real-Time 3D Human Posture Significant Points Estimation, Master Thesis, National Chung-Hsing University, Taiwan, R.O.C., 2009. [5] C. F. Juang, C. M. Chang, J. R. Wu, and D. M. Lee, “Computer vision-based human body segmentation and posture estimation,” IEEE Trans. Syst., Man, and Cyber., Part A: Systems and Humans, vol. 39, no. 1, pp. 119-133, Jan. 2009. [6] A. F. Bobick and A. D. Wilson, “A state-based technique to the representation and recognition of gesture,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 19, pp. 1325-1337, Dec. 1997. [7] T. Starmer, J. Weaver, and A. Pentland, “Real-time American sign language recognition using desk and wearable computer-based video,” IEEE Transactions Pattern Analysis Machine Intelligence, vol. 20, pp. 1317-1375, Dec. 1998. [8] N. Johnson and D. Hogg, “Learning the distribution of object trajectories for event recognition,” Image Vision Computer, vol. 14, no. 8, pp. 609-615, 1996. [9] H. Fujiyoshi and A. J. Lipton, “Real-time human motion analysis by image skeletonization,” Proceedings of IEEE Workshop on Applications of Computer Vision , pp. 15-21, Oct. 1998. [10] Y. Li, M. Songde, and L. Hanqing, “A multiscale morphological method for human posture recognition,” Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 56-61, Apr. 1998. [11] P. Spagnolo, M. Leo, A. Leone, G. Attolico, and A.Distante, “Posture estimation in visual surveillance of archaeological sites,” Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 277-283, July 2003. [12] C. F. Juang and C. M. Chang, “Human body posture classification by a neural fuzzy network and home care system application,” IEEE Trans. Syst., Man, andCyber., Part A: Systems and Humans, vol. 37, no. 6, pp. 984-994, Nov. 2007. [13] I. Haritaoglu, D. Harwood and L. S. Davis, “ real-time surveillance of people and their activities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809 - 830, Aug. 2000. [14] E.Ardizzone, A.Chella, R.Pirrone, “Pose Classification Using Support Vector Machines,” Proceedings of IJCNN, vol.6 ,pp.317-322, 2000.

This thesis proposes a new feature vector for static gesture recognition and the application of a neural fuzzy network as a recognizer. For gesture recognition, the paper considers both the gestures of the two human hands. For each hand, four kinds of main gestures, including arm up and bending, in straight, down are considered. In total, the number of gestures is sixteen. For feature extraction, the thesis first uses a moving object segmentation algorithm to segment human body from the extracted background. A sequence of image processing is used to obtain a complete silhouette. Histograms of horizontal and vertical projections of the silhouette are then used to clip two focused body regions for the two hands. Each focus region is equally divided into fifteen non-overlapping grids. The percentage of human body silhouette pixels in each grid is found, generating a total of sixteen features for a hand. Each of the two fifteen dimensional feature vectors is fed as inputs to a neural fuzzy network for recognition. Experimental results show that the proposed method recognizes the sixteen gestures with high accuracy. Comparisons with an eigenvector-based feature extraction method and a template-matching recognition method demonstrate the advantage of the proposed recognition method.
其他識別: U0005-1708201014010600
Appears in Collections:電機工程學系所

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