Advertisement

An Infrared and Neuro-Fuzzy-Based Approach for Identification and Classification of Road Markings

  • G. N. Marichal
  • E. J. González
  • L. Acosta
  • J. Toledo
  • M. Sigut
  • J. Felipe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)

Abstract

A low-cost infrared and Neuro-Fuzzy-based approach for identification and classification of road markings is presented in this paper. The main task of the designed prototype, implemented at the University of La Laguna, is to collect information by a set of infrared sensors and to process it in an intelligent mode. As an example, it should inform the driver about the different signs and elements it is driving on, complementing other well-known and widely used security devices. For the identification and classification stages, a Neuro-Fuzzy approach has been chosen; given it is a good option in order to get fuzzy rules and memberships functions in an automatic way.

Keywords

Membership Function Fuzzy Rule Test Pattern Traffic Sign Infrared Sensor 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P., Porta, M.: Artificial Vision in Road Vehicles. In: Proceedings of the IEEE - Special issue on Technology and Tools: Visual Perception (2002)Google Scholar
  2. 2.
    Gupte, S., Masoud, O., Papanikolopoulos, N.P.: Detection and classification of vehicles. IEEE Trans. on Intelligent Transportation Systems 3(1), 37–47 (2002)CrossRefGoogle Scholar
  3. 3.
    Tan, Y.P., Yap, K.H., Wang, L.P. (eds.): Intelligent Multimedia Processing with Soft Computing. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Halgamuge, S., Wang, L.P. (eds.): Classification and Clustering for Knowledge Discovery. Springer, Berlin (2005)MATHGoogle Scholar
  5. 5.
    Gavrila, D.M.: Traffic sign recognition revisited. In: Proc. 21st DAGM Symposium fur Musterekennung, pp. 86–93. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    de la Escalera, A., Armingol, J.M., Pastor, J.M., Rodriguez, F.J.: Visual sign information extraction and identification by deformable models for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 5(2), 57–68 (2004)CrossRefGoogle Scholar
  7. 7.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. On Systems, Man and Cybernetics 15, 116–132 (1985)MATHGoogle Scholar
  8. 8.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13 (1975)MATHCrossRefGoogle Scholar
  9. 9.
    Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall, Englewood Cliffs (1992)MATHGoogle Scholar
  10. 10.
    Hush, D.R., Horne, B.G.: Progress in supervised Neural Networks. IEEE Signal Processing Magazine, 8–34 (1993)Google Scholar
  11. 11.
    Karvonen, J.A.: Baltic Sea ice SAR segmentation and classification using modified pulse-coupled neural networks. IEEE Trans. Geoscience and Remote Sensing 42, 1566–1574 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • G. N. Marichal
    • 1
  • E. J. González
    • 1
  • L. Acosta
    • 1
  • J. Toledo
    • 1
  • M. Sigut
    • 1
  • J. Felipe
    • 1
  1. 1.Dep. Ingeniería de Sistemas y Automática y Arquitectura y Tecnología de Computadores.Universidad de La LagunaSpain

Personalised recommendations