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An Automatic Image Segmentation Algorithm Based on Spiking Neural Network Model

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

Inspired by the structure and behavior of the human visual system, an automatic image segmentation algorithm based on a spiking neural network model is proposed. At first, the image pixel values are encoded into the timing of spikes of neurons using the time-to-first-spike coding strategy. Then the segmentation model of spiking neural networks is applied to generate the matrix of spike timing for the visual image. Finally, using the maximum Shannon entropy as the fitness function of genetic algorithm, the evolved segmentation threshold is obtained to segment the visual image. The experimental results show that the method can obtain the optimum segmentation threshold, and achieve satisfactory segmentation results for different images.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xianghong Lin & Xiangwen Wang

  2. Linyi Branch Corporation, China Telecom, Linyi, 276000, China

    Wenbo Cui

Authors
  1. Xianghong Lin
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  2. Xiangwen Wang
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  3. Wenbo Cui
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Lin, X., Wang, X., Cui, W. (2014). An Automatic Image Segmentation Algorithm Based on Spiking Neural Network Model. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_27

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_27

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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