Abstract
The Pulse Coupled Neural Network (PCNN) had been proposed as a model of visual cortex and a lot of applications to the image processing have been proposed recently. Authors also have been proposed Inhibitory Connected PCNN (IC-PCNN) which shows good performances for the color image processing. In our recent study, we had been shown that the IC-PCNN can obtain successful results for the color image segmentation. In this study, we show the effect of the inhibitory connections to the characteristics of synchronous firing assembly. Here we consider that the results will be a key to find appropriate values of inhibitory connections for the image processing using IC-PCNN. In simulations, we show that the valid domains of inhibitory connections for the color image segmentation exists.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Echorn, R., Reitboeck, H.J., Arndt, M., Dicke, P.: Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex. Neural Computation 2, 293–307 (1990)
Echorn, R.: Neural Mechanisms of Scene Segmentation: Recording from the Visual Cortex Suggest Basic Circuits for Liking Field Model. IEEE Trans. Neural Network 10(3), 464–479 (1999)
Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. Neural Network 10(3), 480–498 (1999)
Lindblad, T., Kinser, J.M.: Image processing using Pulse-Coupled Neural Networks, 2nd edn. Springer, Heidelberg (2005)
Zhou, L., Sun, Y., Zheng, J.: Automated Color Image Edge Detection using Improved PCNN Model. WSEAS Transactions on Computers 7(4), 184–189 (2008)
Xiong, X., Wang, Y., Zhang, X.: Color Image Segmentation using Pulse-Coupled Neural Network for Locusts Detection. In: Proc. of the International Conference on Data Mining, pp. 410–413 (2006)
Kurokawa, H., Kaneko, S., Yonekawa, M.: A color image segmentation using inhibitory connected pulse coupled neural network. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) ICONIP 2008. LNCS, vol. 5507, pp. 776–783. Springer, Heidelberg (2009)
Yonekawa, M., Kurokawa, H.: An automatic parameter adjustment method of Pulse Coupled Neural Network for image segmentation. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5768, pp. 834–843. Springer, Heidelberg (2009)
Yonekawa, M., Kurokawa, H.: The parameter optimization of the pulse coupled neural network for the pattern recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010. LNCS, vol. 6354, Springer, Heidelberg (2010)
Ranganth, H.S., Kuntimad, G.: Perfect image segmentation using pulse coupled neural networks. IEEE Trans. Neural Networks 10(3), 591–598 (1999)
Ishihara, S.: Tests for colour-blindness, Handaya (1917)
Images of the Ishihara color test are able to be found on web pages, for example, http://en.wikipedia.org/wiki/Ishihara_color_test http://www.toledo-bend.com/colorblind/Ishihara.htm etc
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kurokawa, H., Yoshihara, M., Yonekawa, M. (2010). An Effect of Inhibitory Connections on Synchronous Firing Assembly in the Inhibitory Connected Pulse Coupled Neural Network. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)