Image Processing with Spiking Neuron Networks

  • Boudjelal Meftah
  • Olivier Lézoray
  • Soni Chaturvedi
  • Aleefia A. Khurshid
  • Abdelkader Benyettou
Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

Artificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission.

SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation.Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach.

Keywords

Radial Basis Function Edge Detection Peak Signal Noise Ratio Neural Code Hebbian Learning 
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.

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References

  1. 1.
    Ghosh-Dastidar, S., Adeli, H.: Third generation neural networks: Spiking neural networks. In: Yu, W., Sanchez, E.N. (eds.) Advances in Computational Intelligence. AISC, vol. 61, pp. 167–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Paugam-Moisy, H., Bohte, S.M.: Computing with Spiking Neuron Networks. In: Kok, J., Heskes, T. (eds.) Handbook of Natural Computing. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Thorpe, S. J., Delorme, A., VanRullen, R. : Spike-based strategies for rapid processing. Neural Networkss 14(6-7), 715–726 (2001)Google Scholar
  4. 4.
    Wu, Q.X., McGinnity, M., Maguire, L.P., Belatreche, A., Glackin, B.: Processing visual stimuli using hierarchical spiking neural networks. Neurocomputing 71(10-12), 2055–2068 (2008)CrossRefGoogle Scholar
  5. 5.
    Girau, B., Torres-Huitzil, C.: FPGA implementation of an integrate-and-fire LEGION model for image segmentation. In: European Symposium on Artificial Neural Networks, ESANN 2006, pp. 173–178 (2006)Google Scholar
  6. 6.
    Buhmann, J., Lange, T., Ramacher, U.: Image Segmentation by Networks of Spiking Neurons. Neural Computation 17(5), 1010–1031 (2005)MATHCrossRefGoogle Scholar
  7. 7.
    Rowcliffe, P., Feng, J., Buxton, H.: Clustering within Integrate-and-Fire Neurons for Image Segmentation. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 69–74. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Maass, W.: On the relevance neural networks. MIT Press, London (2001)Google Scholar
  9. 9.
    Gerstner, W., Kistler, W.M.: Spiking neuron models. Cambridge University Press (2002)Google Scholar
  10. 10.
    Gerstner, W., Kistler, W.: Mathematical formulations of Hebbian learning. Biological Cybernetics 87, 404–415 (2002)MATHCrossRefGoogle Scholar
  11. 11.
    Maass, W.: Networks of Spiking Neurons: The Third Generation of Neural Network Models. Neural Networks 10(9), 1659–1671 (1997)CrossRefGoogle Scholar
  12. 12.
    Maass, W.: Computing with spiking neurons. In: Maass, W., Bishop, C.M. (eds.) Pulsed Neural Networks, MIT Press, Cambridge (1999)Google Scholar
  13. 13.
    NatschlNager, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Network: Comp. Neural Systems 9(3), 319–332 (1998)CrossRefGoogle Scholar
  14. 14.
    Averbeck, B., Latham, P., Pouget, A.: Neural correlations, population coding and computation. Nature Reviews Neuroscience 7, 358–366 (2006)CrossRefGoogle Scholar
  15. 15.
    Stein, R., Gossen, E., Jones, K.: Neuronal variability: noise or part of the signal? Nature Reviews Neuroscience 6, 389–397 (2005)CrossRefGoogle Scholar
  16. 16.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, Cambridge (2001)MATHGoogle Scholar
  17. 17.
    Butts, D.A., Weng, C., Jin, J., Yeh, C., Lesica, N.A., Alonso, J.M., Stanley, G.B.: Temporal precision in the neural code and the timescales of natural vision. Nature 449, 92–95 (2007)CrossRefGoogle Scholar
  18. 18.
    Bohte, S.M.: The Evidence for Neural Information Processing with Precise Spike-times: A Survey. Natural Computing 3(2), 195–206 (2004)MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    Bohte, S.M., La Poutre, H., Kok, J.N.: Unsupervised clustering with spiking neurons by sparse temporal coding and Multi-Layer RBF Networks. IEEE Transactions on Neural Networks 13(2), 426–435 (2002)CrossRefGoogle Scholar
  20. 20.
    Oster, M., Liu, S.C.: A winner-take-all spiking network with spiking inputs. In: Proceedings of the 11th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2004), vol. 11, pp. 203–206 (2004)Google Scholar
  21. 21.
    Gupta, A., Long, L.N.: Hebbian learning with winner take all for spiking neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1189–1195 (2009)Google Scholar
  22. 22.
    Leibold, C., Hemmen, J.L.: Temporal receptive fields, spikes, and Hebbian delay selection. Neural Networks 14(6-7), 805–813 (2001)CrossRefGoogle Scholar
  23. 23.
    da Silva Simões, A., Costa, A.H.R.: A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function. In: Zaverucha, G., da Costa, A.L. (eds.) SBIA 2008. LNCS (LNAI), vol. 5249, pp. 227–236. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Knesek, E.A.: Roche image analysis system. Acta Cytologica 40(1), 60–66 (1996)CrossRefGoogle Scholar
  25. 25.
    Lezoray, O., Cardot, H.: Cooperation of pixel classification schemes and color watershed: a Study for Microscopical Images. IEEE Transactions on Images Processing 11(7), 738–789 (2002)CrossRefGoogle Scholar
  26. 26.
    Mouroutis, T., Roberts, S.J., Bharath, A.A.: Robust cell nuclei segmentation using statistical modeling. BioImaging 6, 79–91 (1998)CrossRefGoogle Scholar
  27. 27.
    Wu, H.S., Barba, J., Gil, J.: Iterative thresholding for segmentation of cells from noisy images. J. Microsc. 197, 296–304 (2000)CrossRefGoogle Scholar
  28. 28.
    Karlsson, A., Stråhlén, K., Heyden, A.: Segmentation of Histopathological Sections Using Snakes. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 595–602. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  29. 29.
    Papanicolaou, G.N.: A new procedure for staining vaginal smears. Science 95, 432 (1942)CrossRefGoogle Scholar
  30. 30.
    Meftah, B., Benyettou, A., Lezoray, O., Wu, Q.X.: Image clustering with spiking neuron network. In: IEEE World Congress on Computational Intelligence, International Joint Conference on Neural Networks (IJCNN 2008), pp. 682–686 (2008)Google Scholar
  31. 31.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int Conf. Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  32. 32.
    Meurie, C., Lezoray, O., Charrier, C., Elmoataz, A.: Combination of multiple pixel classifiers for microscopic image segmentation. IASTED International Journal of Robotics and Automation 20(2), 63–69 (2005)Google Scholar
  33. 33.
    Meftah, B., Lezoray, O., Lecluse, M., Benyettou, A.: Cell Microscopic Segmentation with Spiking Neuron Networks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part I. LNCS, vol. 6352, pp. 117–126. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Boudjelal Meftah
    • 1
  • Olivier Lézoray
    • 2
  • Soni Chaturvedi
    • 3
  • Aleefia A. Khurshid
    • 3
  • Abdelkader Benyettou
    • 4
  1. 1.Equipe EDTECUniversité de MascaraMascaraAlgérie
  2. 2.GREYC UMR CNRS 6072Université de Caen Basse-NormandieCaenFrance
  3. 3.Priyadarshini Institute of Engineering and TechnologyNagpurIndia
  4. 4.Laboratoire Signal Image et ParoleUniversité Mohamed BoudiafOranAlgérie

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