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Automatic Image Segmentation Based on a Simplified Pulse Coupled Neural Network

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

Recent researches indicate that pulse coupled neural network (PCNN) can be effectively utilized in image segmentation. However, the near optimal parameter set should always be predetermined to achieve desired segmentation result for different images, which impedes its application for segmentation of various images. So far as that is concerned, there is no method of adaptive parameter determination for automatic real-time image segmentation. To solve the problem, this paper brings forward a new automatic segmentation method based on a simplified PCNN with the parameters determined by images’ spatial and grey characteristics adaptively. The proposed algorithm is applied to different images and the experimental results demonstrate its validity.

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References

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116024, Liaoning Province, P.R. China

    Yingwei Bi, Tianshuang Qiu, Xiaobing Li & Ying Guo

Authors
  1. Yingwei Bi
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  2. Tianshuang Qiu
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  3. Xiaobing Li
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  4. Ying Guo
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Bi, Y., Qiu, T., Li, X., Guo, Y. (2004). Automatic Image Segmentation Based on a Simplified Pulse Coupled Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_64

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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