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Segmentation and Edge Detection Based on Spiking Neural Network Model

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Abstract

The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. Artificial neural networks have been well developed so far. First two generations of neural networks have 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 paper, we present how SNN can be applied with efficacy in image segmentation and edge detection. Results obtained confirm the validity of the approach.

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References

  1. Tavares JM (2010) A review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Engin 13(2): 235–246

    Article  Google Scholar 

  2. Liew AW, Yan H, Law NF (2005) Image segmentation based on adaptive cluster prototype estimation. IEEE Trans Fuzzy Syst 13(4): 444–453

    Article  Google Scholar 

  3. Deng Y, Manjunath BS (2001) Unsupervised segmentation of color texture regions in images and video. IEEE Trans Pattern Anal Mach Intell 23(8): 800–810

    Article  Google Scholar 

  4. Senthilkumaran N, Rajesh R (2009) Edge detection techniques for image segmentation and a survey of soft computing approaches. Int J Recent Trends Eng 1(2): 250–254

    Google Scholar 

  5. Freixenet J, Munoz X, Raba D, Marti D, Cufi X (2002) Yet another survey on image segmentation: region and boundary information integration. In: ECCV 2002. LNCS, vol 2352. Springer, Heidelberg, pp 408–422

  6. Melkemi KE, Batouche M, Foufou S (2006) A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics. Pattern Recogn Lett 27(11): 1230–1238

    Article  Google Scholar 

  7. Xu C, Pham D, Prince J (2000) Image segmentation using deformable models. In: Fitzpatrick JM, Sonka M (eds) Handbook of medical imaging. Medical image processing and analysis, vol 2, SPIE Press, pp 129–174

  8. Ghosh-Dastidar S, Adeli H (2009) Third generation neural networks: spiking neural networks. Adv Comput Intell AISC(61): 167–178

    Article  Google Scholar 

  9. Paugam-Moisy H, Bohte SM (2009) Computing with spiking neuron networks. In: Kok J, Heskes T (eds) Handbook of natural computing (40 pages—to appear). Springer Verlag, Heidelberg

    Google Scholar 

  10. Thorpe SJ, Delorme A, VanRullen R (2001) Spike-based strategies for rapid processing. Neural Netw 14(6–7): 715–726

    Article  Google Scholar 

  11. Wu QX, McGinnity M, Maguire LP, Belatreche A, Glackin B (2008) Processing visual stimuli using hierarchical spiking neural networks. Neurocomputing 71(10–12): 2055–2068

    Article  Google Scholar 

  12. Girau B, Torres-Huitzil C (2006) FPGA implementation of an integrate-and-fire LEGION model for image segmentation. In: European symposium on artificial neural networks ESANN’2006, Bruges, Belgium 26–28 April 2006, pp 173–178

  13. Buhmann J, Lange T, Ramacher U (2005) Image segmentation by networks of spiking neurons. Neural Comput 17(5): 1010–1031

    Article  MATH  Google Scholar 

  14. Rowcliffe P, Feng J, Buxton H (2002) Clustering within integrate-and-fire neurons for image segmentation. In: Artificial neural networks ICANN 2002, Madrid, Spain, 28–30 August 2002. LNCS, vol 2415. Springer, pp 69–74

  15. Maass W (2001) On the relevance neural networks. MIT-Press, London

    Google Scholar 

  16. Gerstner W, Kistler WM (2002) Spiking neuron models, single neurons, populations, plasticity. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  17. NatschlNager T, Ruf B (1998) Spatial and temporal pattern analysis via spiking neurons. Netw Comp Neural Syst 9(3): 319–332

    Article  Google Scholar 

  18. Averbeck B, Latham P, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7: 358–366

    Article  Google Scholar 

  19. Stein R, Gossen E, Jones K (2005) Neuronal variability: noise or part of the signal? Nat Rev Neurosci 6: 389–397

    Article  Google Scholar 

  20. Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. The MIT Press, Cambridge

    MATH  Google Scholar 

  21. Butts DA, Weng C, Jin J, Yeh C, Lesical NA, Alonso JM, Stanley GB (2007) Temporal precision in the neural code and the timescales of natural vision. Nature 449: 92–95

    Article  Google Scholar 

  22. Bohte SM (2004) The evidence for neural Information processing with precise spike-times: a survey. Nat Comput 3(2): 195–206

    Article  MathSciNet  MATH  Google Scholar 

  23. Bohte SM, La Poutre H, Kok JN (2002) Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks. IEEE Trans Neural Netw 13(2): 426–435

    Article  Google Scholar 

  24. Oster M, Liu SC (2004) A winner-take-all spiking network with spiking inputs. In: Proceedings of the 11th IEEE international conference on electronics, circuits and systems (ICECS 2004), Tel-Aviv, Israel, 13–15 December 2004, vol 11, pp 203–206

  25. Gupta A, Long LN (2009) Hebbian learning with winner take all for spiking neural networks. In: IEEE international joint conference on neural networks (IJCNN), pp 1189–1195

  26. Gerstner W, Kistler W (2002) Mathematical formulations of Hebbian learning. Biol Cybernet 87: 404–415

    Article  MATH  Google Scholar 

  27. Leibold C, Hemmen JL (2001) Temporal receptive fields, spikes, and Hebbian delay selection. Neural Netw 14(6–7): 805–813

    Article  Google Scholar 

  28. Simoes AS, Reali Costa AH (2008) A learning function for parameter reduction in spiking neural networks with radial basis function. In: Proceedings of the 19th symposium of artificial intelligence (SBIA 2008), Savador, Brazil, 26–30 October 2008. Proceedings, vol 5249. Springer, pp 227–236

  29. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, In: Proceedings of the 8th international conference on computer vision, Vancouver, British Columbia, Canada, 7–14 July 2001, vol 2, pp 416–423

  30. Meftah B, Benyettou A, Lezoray O, Wu QX (2008) Image clustering with spiking neuron network. In: IEEE world congress on computational intelligence, international joint conference on neural networks (IJCNN’08), Hong Kong, 1–6 June 2008, pp 682–686

  31. Meurie C, Lezoray O, Charrier C, Elmoataz A (2005) Combination of multiple pixel classifiers for microscopic image segmentation. IASTED Int J Robot Auto 20(2): 63–69

    Google Scholar 

  32. Chicurel M (2002) Cell migration research is on the move. Science 295: 606–609

    Article  Google Scholar 

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Authors and Affiliations

  1. Equipe EDTEC, Université de Mascara, Mascara, Algéria

    B. Meftah

  2. Université de Caen Basse-Normandie, GREYC UMR CNRS 6072, 6 Bd. Maréchal Juin, 14050, Caen, France

    O. Lezoray

  3. Laboratoire Signal Image et Parole (SIMPA), Université Mohamed Boudiaf (USTO), Oran, Algéria

    A. Benyettou

Authors
  1. B. Meftah
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  2. O. Lezoray
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  3. A. Benyettou
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Correspondence to B. Meftah.

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Meftah, B., Lezoray, O. & Benyettou, A. Segmentation and Edge Detection Based on Spiking Neural Network Model. Neural Process Lett 32, 131–146 (2010). https://doi.org/10.1007/s11063-010-9149-6

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  • DOI: https://doi.org/10.1007/s11063-010-9149-6

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