Skip to main content
SpringerLink
Log in

A Novel CNN Template Design Method Based on GIM

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

Included in the following conference series:

  • 970 Accesses

Abstract

In this paper, a kind of relation between CNN (cellular neural network) and GIM (Gibbs image model) is noted. Based on this relation, a new approach for CNN’s template design is proposed, this approach is valid to many questions that could be processed with GIM, such as segmentation, edge detection and restoration. We also discuss the learning algorithm and hardware annealing jointed with the new approach. Simulations of some examples are shown in order to validate effectiveness of new approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Chua, L.O., Yang, L.: Cellular Neural Network: Theory. IEEE Trans. Circuits Syst. 35, 1257–1272 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  2. Chua, L.O., Yang, L.: Cellular Neural Network: Applications. IEEE Trans. Circuits Syst. 35, 1257–1272 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  3. Gu, X., Yu, D., Zhang, L.: Image Thinning Using PCNN. Pattern Recognition Letters 25, 1075–1084 (2004)

    Article  Google Scholar 

  4. Derin, H., Elliot, H.: Modeling and Segmentation of Noisy Textured Image Using GRF. IEEE Trans. on PAMI 9, 39–55 (1987)

    Google Scholar 

  5. Tan, L.: A Comparative Cost Function Approach to Edge Detection. IEEE Tran. on SMC 9, 1337–1349 (1989)

    Google Scholar 

  6. Holland, J.H.: Adaptation in Natural and Artificial Systems. The university of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  7. Pitas, I.: Markov Image Model for Image Labeling and Edge Detection. Signal Processing 15, 365–374 (1988)

    Article  Google Scholar 

  8. Bang, S.H., Sheu, B.J., Wu, H.Y.: Optimal Solutions for Cellular Neural Networks by Paralleled Hardware Annealing. IEEE Trans. on NN 28, 440–453 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics, Peking University, Beijing, 100871, China

    Jianye Zhao, Hongling Meng & Daoheng Yu

Authors
  1. Jianye Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongling Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daoheng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhao, J., Meng, H., Yu, D. (2005). A Novel CNN Template Design Method Based on GIM. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_71

Download citation

  • DOI: https://doi.org/10.1007/11427391_71

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation