Advertisement

Using Self-Organizing Maps for Intelligent Camera-Based User Interfaces

  • Zorana Banković
  • Elena Romero
  • Javier Blesa
  • José M. Moya
  • David Fraga
  • Juan Carlos Vallejo
  • Álvaro Araujo
  • Pedro Malagón
  • Juan-Mariano de Goyeneche
  • Daniel Villanueva
  • Octavio Nieto-Taladriz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

The area of Human-Machine Interface is growing fast due to its high importance in all technological systems. The basic idea behind designing Human-Machine interfaces is to enrich the communication with the technology in a natural and easy way. Gesture interfaces are a good example of transparent interfaces. Such interfaces must perform the action the user wants, so that the proper gesture recognition is of the highest importance. However, most of the systems based on gesture recognition use complex methods requiring high-resource devices. In this work we propose to model gestures capturing their temporal properties, significantly reducing the storage requirements, and using self-organizing maps for their classification. The main advantage of the approach is its simplicity, which enables the implementation using devices with limited resources, and therefore low cost. First testing results demonstrate its high potential.

Keywords

Gesture recognition intelligent environments self-organizing maps 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ishikawa, M., Sasaki, N.: Gesture Recognition based on SOM using Multiple Sensors. In: 9th International Conference on Neural Information Processing, pp. 1300–1304. IEEE Xplore (2002)Google Scholar
  2. 2.
    Shimada, A., Taniguchi, R.: Gesture Recognition Using Sparse Code of Hierarchical SOM. In: 19th International Conference on Pattern Recognition, pp. 1–4. IEEE Xplore (2008)Google Scholar
  3. 3.
    Caridakis, G., Karpouzis, K., Drosopoulos, A.I., Kollias, S.D.: SOMM: Self organizing Markov map for gesture recognition. Pattern Recognition Letters 31(1), 52–59 (2010)CrossRefGoogle Scholar
  4. 4.
    Caridakis, G., Karpouzis, K., Pateritsas, C., Drosopoulos, A.I., Stafylopatis, A., Kollias, S.D.: Hand trajectory based gesture recognition using self-organizing feature maps and Markov models. In: ICME 2008, pp. 1105–1108 (2008)Google Scholar
  5. 5.
    Rieck, K., Laskov, P.: Linear Time Computation of Similarity for Sequential Data. Journal of Machine Learning Research 9, 23–48 (2008)Google Scholar
  6. 6.
    Rojas, R.: Neural Networks. Springer, Berlin (1996)MATHGoogle Scholar
  7. 7.
    Littmann, E., Drees, A., Ritter, H.: Neural Recognition of Human Pointing Gestures in Real Images. In: Neural Processing Letters, pp. 61–71. Kluwer Academic Publishers, Dordrecht (1996)Google Scholar
  8. 8.
    Vleugels, J.M., Kok, J.N., Overmars, M.H.: A self-organizing neural network for robot motion planning. In: Gielen, S., Kappen, B. (eds.) ICANN 1993 Art. Neural Networks Conf. Proc., pp. 281–284. Springer, Heidelberg (1993)Google Scholar
  9. 9.
    Aupetit, M., Couturier, P., Massote, P.: Function Approximation with Continuous Self-Organizing Maps Using Neighboring Influence Interpolation. In: Proc. of Neural Computation (NC 2000), Berlin, Germany (May 2000)Google Scholar
  10. 10.
    Lane Thames, J., Abler, R., Saad, A.: Hybrid intelligent systems for network security. In: ACM Southeast Regional Conference, Proceedings of the 44th annual Southeast regional conference, pp. 286–289 (2006)Google Scholar
  11. 11.
    Muñoz, A., Muruzábal, J.: Self-Organizing Maps for Outlier Detection. Neurocomputing 18(1-3), 33–60 (1998)CrossRefGoogle Scholar
  12. 12.
    SOM Algorithm, http://www.ai-junkie.comñ/ann/som/som2.htmlGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Zorana Banković
    • 1
  • Elena Romero
    • 1
  • Javier Blesa
    • 1
  • José M. Moya
    • 1
  • David Fraga
    • 1
  • Juan Carlos Vallejo
    • 1
  • Álvaro Araujo
    • 1
  • Pedro Malagón
    • 1
  • Juan-Mariano de Goyeneche
    • 1
  • Daniel Villanueva
    • 1
  • Octavio Nieto-Taladriz
    • 1
  1. 1.Dep. Ingeniería ElectrónicaUniversidad Politécnica de MadridMadridSpain

Personalised recommendations