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 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
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)
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)
Kohonen, T.: Self-Organizing Maps. Information Sciences, vol. 30, 3rd edn. Springer, New York (2001)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-46672-9_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46671-2
Online ISBN: 978-3-319-46672-9
eBook Packages: Computer ScienceComputer Science (R0)