Gesture Spotting by Using Vector Distance of Self-organizing Map

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• 关键词：Self organizing map (SOM) ; Hebb learning ; Hand sign recognition ; Gesture spotting
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9948
• 期：1
• 页码：419-426
• 全文大小：2,512 KB
• 参考文献：1.Pavlovic, V.I., Sharma, R., Huang, T.S.: Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans. Pattern Anal. Mach. Intell. 19, 677–695 (1997)CrossRef
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  4.Bobick, A.F., Wilson, A.D.: A state-based approach to the representtion and recognition of gesture. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1325–1337 (1997)CrossRef
  5.Yang, H.-H., Ahuja, N., Tabb, M.: Extraction of 2D motion trajectories and its application to hand gesture recogntion. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1061–1074 (2002)CrossRef
  6.Kohonen, T.: Self-Organizing Maps. Information Sciences, vol. 30, 3rd edn. Springer, New York (2001)CrossRef MATH
  7.Hikawa, H., Kaida, K.: Novel FPGA implementation of hand sign recognition system with SOM-hebb classifier. IEEE Trans. Circ. Syst. Video Technol. 25(1), 153–166 (2015)CrossRef
  
• 作者单位：Yuta Ichikawa (19)
  Shuji Tashiro (19)
  Hidetaka Ito (19)
  Hiroomi Hikawa (19)
  
  19. Department of Electrical and Electronic Engineering, Kansai University, Suita, Japan
  
• 丛书名：Neural Information Processing
• ISBN：978-3-319-46672-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9948

文摘

This paper proposes a dynamic hand gesture recognition algorithm with a function of gesture spotting. The algorithm consists of two self-organizing maps (SOMs) and a Hebb learning network. Feature vectors are extracted from input images, and these are fed to one of the SOMs and a vector that represents the sequence of postures in the given frame is generated. Using this vector, gesture classification is performed using another SOM. In the SOM, the vector distance between the input vector and the winner neuron’s weight vector is used for the gesture spotting. The following Hebb network identifies the gesture class. The experimental results show that the system recognizes eight gestures with the accuracy of 95.8 %.