Skip to main content
SpringerLink
Log in

A New Pulse-Coupled Neural Network Algorithm for Image Segmentation

  • Conference paper
Soft Computing as Transdisciplinary Science and Technology

Part of the book series: Advances in Soft Computing ((AINSC,volume 29))

  • 953 Accesses

5 Conclusion

The new PCNN algorithm introduced here is an autonomous image segmentation algorithm. This algorithm outweighs conventional PCNN algorithms in the following aspects: PCNN’s iterative computation can be terminated automatically; noise can be reduced effectively by the algorithm of pulsing-pattern-based noise detection and adaptive synaptic modification; all information contained in PCNN’s output sequence can be utilized. Furthermore, our algorithm inherits the advantages of PCNN, such as parallel processing and parameter robustness. Experiments show that this algorithm performs well in different kinds of images. As for future research, it would be very attractive to apply this algorithm to the field of pattern recognition.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Eckhorn R, Reitboeck H J, Arndt M, etal. Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex. Neural Computation, 1990, 2: 293–307.

    Article  Google Scholar 

  2. Johnson J L. Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance for images. Appl. Opt, 1994, 33(26): 6239–6253.

    Google Scholar 

  3. Jun Chen, Kosei Ishimura, Mitsuo Wada, Moving Object Extraction Using Multi-Tiered Pulse-Coupled Neural Network. SICE Annual Conference in Sapporo, Japan, 2004: 2843–2848.

    Google Scholar 

  4. Johnson J L, Padgett M L. PCNN models and applications. IEEE Transactions on Neural Networks, 1999, 10(3): 480–498.

    Article  Google Scholar 

  5. Thomas Lindblad, Jason M. Kinser. Image Processing using Pulse-Coupled Neural Network. Springer-Verlag London Limited 1998.

    Google Scholar 

  6. Thomas Lindblad, Jason M. Kinser. Inherent Features of Wavelets and Pulse Coupled Networks. IEEE Transactions on Neural Networks, 1999, 10(3):607–614.

    Article  Google Scholar 

  7. Stewart R D, Fermin I, Opper M. Region growing with pulse coupled neural networks: an alternative to seed region growing, IEEE Transactions on Neural Networks, 2002, 13(6): 1557–1562.

    Article  Google Scholar 

  8. Liu, J., Yang, Y.-H.: Multiresolution Color Image Segmentation. IEEE Transactions on PAMI, 1994, 16(7): 689–700

    Google Scholar 

  9. Adams R, Bischof L. Seed region growing. IEEE Trans. Pattern Anal. Machine Intell., 1994, 16: 634–643.

    Article  Google Scholar 

  10. Bir Bhanu, Stephanie Fonder. Functional template-based SAR image segmentation. Pattern Recognition 2004 (37): 61–77.

    Article  Google Scholar 

  11. Ali El Zaart, Djemel Ziou, Shengrui Wang, Qingshan Jiang. Segmentation of SAR images. Pattern Recognition 2002 (35): 713–724.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

    Jun Chen, Mitsuo Wada & Kosei Ishimura

  2. Chu Kechen Honors College, Zhejiang University, Hangzhou, China

    Jun Chen

Authors
  1. Jun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mitsuo Wada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kosei Ishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Oklahoma State University, North Greenwood Avenue 700, 74106, Tulsa, USA

    Ajith Abraham

  2. Dept. of Computer Science and Systems Engineering, Muroran Institute of Technology, Mizumoto-cho 27-1, 050-8585, Muroran, Hokkaido, Japan

    Yasuhiko Dote

  3. Dept. of Computational Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, 464-8601, Nagoya, Aichi-ken, Japan

    Takeshi Furuhashi

  4. Institut für Sicherheits-/Prüftechnik, Fraunhofer IPK Berlin, Pascalstr. 8-9, 10587, Berlin, Germany

    Mario Köppen

  5. Division of Complex Systems Technology Graduate School of Engineering, University of Hokkaido, N-13, W-8 060 KITA-KU, Sapporo, Japan

    Azuma Ohuchi

  6. PRESTO Japan Science & Technology Corp. GSSM, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, 12-0012, Tokyo, Japan

    Yukio Ohsawa

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Chen, J., Wada, M., Ishimura, K. (2005). A New Pulse-Coupled Neural Network Algorithm for Image Segmentation. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-32391-0_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25055-5

  • Online ISBN: 978-3-540-32391-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation