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Gesture Spotting by Using Vector Distance of Self-organizing Map

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

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Abstract

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 %.

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References

  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)

    Article  Google Scholar 

  2. Triesch, J., von der Malsburg, C.: A system for person-independent hand posture recognition against complex backgrounds. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1449–1453 (2001)

    Article  Google Scholar 

  3. Hoshino, K., Tanimoto, T.: Realtime hand posture estimation with self-organizing map for stable robot control. IEICE Trans. Inf. Syst. E89–D(6), 1813–1819 (2006)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Kohonen, T.: Self-Organizing Maps. Information Sciences, vol. 30, 3rd edn. Springer, New York (2001)

    Book  MATH  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Kansai University, Suita, Japan

    Yuta Ichikawa, Shuji Tashiro, Hidetaka Ito & Hiroomi Hikawa

Authors
  1. Yuta Ichikawa
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  2. Shuji Tashiro
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  3. Hidetaka Ito
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  4. Hiroomi Hikawa
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Corresponding author

Correspondence to Hiroomi Hikawa .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Ichikawa, Y., Tashiro, S., Ito, H., Hikawa, H. (2016). Gesture Spotting by Using Vector Distance of Self-organizing Map. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_47

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  • DOI: https://doi.org/10.1007/978-3-319-46672-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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