Skip to main content
Book cover

Ibero-American Conference on Artificial Intelligence

IBERAMIA 2008: Advances in Artificial Intelligence – IBERAMIA 2008 pp 103–112Cite as

A Dipolar Competitive Neural Network for Video Segmentation

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 5290)

Abstract

This paper present a video segmentation method which separate pixels corresponding to foreground from those corresponding to background. The proposed background model consists of a competitive neural network based on dipoles, which is used to classify the pixels as background or foreground. Using this kind of neural networks permits an easy hardware implementation to achieve a real time processing with good results. The dipolar representation is designed to deal with the problem of estimating the directionality of data. Experimental results are provided by using the standard PETS dataset and compared with the mixture of Gaussians and background subtraction methods.

Keywords

  • Input Pattern
  • Synaptic Potential
  • Video Segmentation
  • Shadow Detection
  • Background Subtraction Method

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-540-88309-8_11
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-540-88309-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.00
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1337–1342 (2003)

    CrossRef  Google Scholar 

  2. Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., Russell, S.: Towards robust automatic traffic scene analysis in real-time. In: Proceedings of the International Conference on Pattern Recognition (1994)

    Google Scholar 

  3. Lo, B., Velastin, S.: Automatic congestion detection system for underground platforms. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 158–161 (2001)

    Google Scholar 

  4. Wren, C., Azarbayejani, A., Darrell, T., Pentl, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 780–785 (1997)

    CrossRef  Google Scholar 

  5. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1999)

    Google Scholar 

  6. Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)

    CrossRef  Google Scholar 

  7. García-Bernal, M., Muñoz, J., Gómez-Ruiz, J., Ladrón De Guevara-Lopez, I.: A competitive neural network based on dipoles. In: Mira, J., Álvarez, J.R. (eds.) IWANN 2003. LNCS, vol. 2686. Springer, Heidelberg (2003)

    CrossRef  Google Scholar 

  8. Oliveira, P., Romero, R.: Improvements on ica mixture models for image pre-processing and segmentation. Neurocomputing (in press, 2008)

    Google Scholar 

  9. Mu-Chun, S., Hung, C.H.: A neural-network-based approach to detecting rectangular objects. Neurocomputing 71(1-3), 270–283 (2007)

    CrossRef  Google Scholar 

  10. Meyer-Base, A., Thummler, V.: Local and global stability analysis of an unsupervised competitive neural network. IEEE Transactions on Neural Networks 19(2), 346–351 (2008)

    CrossRef  Google Scholar 

  11. Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proceedings of International Conference on Computer Vision (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Málaga, Málaga, Spain

    R. M. Luque, E. Dominguez & E. J. Palomo

  2. Department of Applied Mathematics, University of Málaga, Málaga, Spain

    D. López-Rodríguez

Authors
  1. R. M. Luque
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. López-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Dominguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. J. Palomo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. ICREA & Universitat Pompeu Fabra, Paseo de Circumvalacion 8, 08003, Barcelona, Spain

    Hector Geffner

  2. IST-UTL and INESC-ID, Av. Prof. Cavaco Silva - Taguspark, 2744-016, Porto Salvo, Portugal

    Rui Prada

  3. ADETTI/ISCTE and ISCTE, Lisbon University Institute, Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Isabel Machado Alexandre

  4. ADETTI/ISCTE and ISCTE, Lisbon University Institute, , Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Nuno David

Rights and permissions

Reprints and Permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luque, R.M., López-Rodríguez, D., Dominguez, E., Palomo, E.J. (2008). A Dipolar Competitive Neural Network for Video Segmentation. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88309-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88308-1

  • Online ISBN: 978-3-540-88309-8

  • eBook Packages: Computer ScienceComputer Science (R0)