An Adaptive Image Segmentation Method Based on a Modified Pulse Coupled Neural Network

  • Min Li
  • Wei Cai
  • Xiao-yan Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)


Pulse-coupled neural network (PCNN) based on Eckhorn’s model of the cat visual cortex has great significant advantage in image segmentation. However, the segmented performance depends on the suitable PCNN parameters, which are tuned by trial so far. Focusing on the famous difficult problem of PCNN, this paper establishes a modified PCNN, and proposes adaptive PCNN parameters determination algorithm based on water region area. Experimental results on image segmentation demonstrate its validity and robustness.


Image Segmentation Peak Point Pulse Couple Neural Network Segmented Performance Bottom Point 
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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  1. 1.
    Eckhorn, R., Reit Boeck, H.J., et al.: Feature linking via synchronization among distributed assemblies: simulation of results form cat visual cortex. Neural Computation 2, 293–307 (1990)CrossRefGoogle Scholar
  2. 2.
    Kuntimad, G., Ranganath, H.S.: Perfect image segmentation using pulse coupled neural networks. IEEE Trans. Neural Networks 10, 591–598 (1999)CrossRefGoogle Scholar
  3. 3.
    Ma, Y.D., Dai, R.L., Li, L.: Automated image segmentation using pulse coupled neural networks and image’s entropy. Journal of China Institute of Communications 23, 46–51 (2002)Google Scholar
  4. 4.
    Liu, Q., Ma, Y.D., Qian, Z.H.B.: Automated image segmentation using improved PCNN model based on cross-entropy. Journal of Image and Graphics 10, 579–584 (2005)Google Scholar
  5. 5.
    Gu, X.D., Guo, S.D., Yu, D.H.: A new approach for automated image segmentation based on unit-linking PCNN. In: Proceedings of the first International Conference on Machine learning and Cybernetics, Beijing, China, pp. 175–178 (2002)Google Scholar
  6. 6.
    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
  7. 7.
    Bi, Y.W., Qiu, T.S.H.: An adaptive image segmentation method based on a simplified PCNN. Acta Electronica Sinica 33, 647–650 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Min Li
    • 1
  • Wei Cai
    • 1
  • Xiao-yan Li
    • 2
  1. 1.Xi’an Research Inst. of Hi-Tech Hongqing TownShaanxi ProvinceP.R.C.
  2. 2.Academy of Armored Force Engineering Department of Information EngineeringBeijingP.R.C.

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