Skip to main content

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

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2010)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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)

    Article  Google Scholar 

  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. Rieck, K., Laskov, P.: Linear Time Computation of Similarity for Sequential Data. Journal of Machine Learning Research 9, 23–48 (2008)

    Google Scholar 

  6. Rojas, R.: Neural Networks. Springer, Berlin (1996)

    MATH  Google Scholar 

  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. 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. 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. 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. Muñoz, A., Muruzábal, J.: Self-Organizing Maps for Outlier Detection. Neurocomputing 18(1-3), 33–60 (1998)

    Article  Google Scholar 

  12. SOM Algorithm, http://www.ai-junkie.comñ/ann/som/som2.html

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Banković, Z. et al. (2010). Using Self-Organizing Maps for Intelligent Camera-Based User Interfaces. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_60

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13803-4_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13802-7

  • Online ISBN: 978-3-642-13803-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics