Abstract
A technique for the image recognition is major issue in the image processing and the image recognition method using pulse coupled neural network (PCNN) have been studied as one of the valid method. The most outstanding feature of the method using PCNN is that the method is valid for the rotation, magnification and shrinking of the image. Also, the good compatibility to the hardware implementation is significant feature of the PCNN. In our previous study, we proposed the GA based learning method for the PCNN parameters which enable the reliable results of image recognition. In this study, we evaluate the image recognition method using PCNN with our learning method. In the simulation results, we clarify the characteristics of recognition rate to the number of the images to be learned using our proposed learning method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
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)
Engel, A.K., Kreiter, A.K., König, P., Singer, W.: Synchronization of oscillatory neuronal responses between striate and extrastriate visual cortical areas of cat. Proc. Natl. Acad. Sci. 88, 6048–6052, USA (1991)
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)
Ranganth, H.S., Kuntimad, G.: Image segmentation using pulse coupled neural networks. In: Proc. Int. Conf. Neural Networks, Orlando,FL, vol. 2, pp. 1285–1290 (1994)
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)
Lindblad, T., Kinser, J.M.: Image processing using Pulse-Coupled Neural Networks, 2nd edn. Springer, Heidelberg (2005)
Gu, X.-D., Wang, Y.-y., Zhang, L.-M.: Object detection using unit-linking PCNN image icons. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 616–622. Springer, Heidelberg (2006)
Mahgoub, A.G., et al.: An Intersecting Cortical Model Based Framework for Human Face Recognition. Journal of Systemics, Cybernetics and Informatics 6(2), 88–93 (2008)
Vega-Pineda, J., Chacon-Murguia, M.I., Camarillo-Cisneros, R.: Synthesis of Pulse-Coupled Neural Networks in FPGAs for Real-Time Image Segmentation. In: Proc. of IJCNN, pp. 8167–8171 (2006)
Yonekawa, M., Kurokawa, H.: The parameter optimization of the pulse coupled neural network for the pattern recognition. In: DILS 2010, vol. 6254, pp. 110–113 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yonekawa, M., Kurokawa, H. (2011). An Evaluation of the Image Recognition Method Using Pulse Coupled Neural Network. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-21735-7_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21734-0
Online ISBN: 978-3-642-21735-7
eBook Packages: Computer ScienceComputer Science (R0)