Hand Gesture Recognition Via a New Self-organized Neural Network

  • E. Stergiopoulou
  • N. Papamarkos
  • A. Atsalakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

A new method for hand gesture recognition is proposed which is based on an innovative Self-Growing and Self-Organized Neural Gas (SGONG) network. Initially, the region of the hand is detected by using a color segmentation technique that depends on a skin-color distribution map. Then, the SGONG network is applied on the segmented hand so as to approach its topology. Based on the output grid of neurons, palm geometric characteristics are obtained which in accordance with powerful finger features allow the identification of the raised fingers. Finally, the hand gesture recognition is accomplished through a probability-based classification method.

Keywords

Gesture Recognition Hopfield Neural Network Hand Gesture Recognition Color Segmentation Hand Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chung-Lin, H., Wen-Yi, H.: Sign language recognition using model-based tracking and a 3D Hopfield neural network. Machine Vision and Applications 10, 292–307 (1998)CrossRefGoogle Scholar
  2. 2.
    Chung-Lin, H., Sheng-Hung, J.: A model-based hand gesture recognition system. Machine Vision and Applications 12, 243–258 (2001)CrossRefGoogle Scholar
  3. 3.
    Xiaoming, Y., Ming, X.: Estimation of the fundamental matrix from uncalibrated stereo hand images for 3D hand gesture recognition. Pattern Recognition 36, 567–584 (2003)CrossRefGoogle Scholar
  4. 4.
    Herpers, R., Derpanis, K., MacLean, W.J., Verghese, G., Jenkin, M., Milios, E., Jepson, A., Tsotsos, J.K.: SAVI: an actively controlled teleconferencing system. Image snd Vision Computing 19, 793–804 (2001)CrossRefGoogle Scholar
  5. 5.
    O’ Mara David T.J.: Automated Facial Metrology. Ph.D. Thesis, University of Western Australia, Department of Computer Science and Software Engineering (2002)Google Scholar
  6. 6.
    Douglas, C., King, N.N.: Face segmentation using skin color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology, 551–564 (1999)Google Scholar
  7. 7.
    Douglas, C., King, N.N.: Locating facial region of a head–and–shoulders color image. In: Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 124–129 (April 1998)Google Scholar
  8. 8.
    Antonis, A.: Colour Reduction in Digital Images. Ph.D. Thesis, Democritus University of Thrace, Department of Electrical and Computer Engineering (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • E. Stergiopoulou
    • 1
  • N. Papamarkos
    • 1
  • A. Atsalakis
    • 1
  1. 1.Image Processing and Multimedia Laboratory, Department of Electrical & Computer EngineeringDemocritus University of ThraceXanthiGreece

Personalised recommendations