Skip to main content

An Evaluation of the Image Recognition Method Using Pulse Coupled Neural Network

  • Conference paper
Book cover Artificial Neural Networks and Machine Learning – ICANN 2011 (ICANN 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6791))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Johnson, J.L., Padgett, M.L.: PCNN Models and Applications. IEEE Trans. Neural Network 10(3), 480–498 (1999)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. 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)

    Chapter  Google Scholar 

  7. Lindblad, T., Kinser, J.M.: Image processing using Pulse-Coupled Neural Networks, 2nd edn. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  8. 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)

    Chapter  Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics