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International Symposium on Parallel Architectures, Algorithms and Programming

PAAP 2020: Parallel Architectures, Algorithms and Programming pp 187–199Cite as

A Fast Algorithm for Image Segmentation Based on Global Cosine Fitting Energy Model

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1362))

Abstract

Image segmentation plays an important role in computer vision and image processing. Level set model is a classical image segmentation method. During level set evolution, almost all the level set energy minimization of image segmentation are based on the gradient descent method and the finite difference scheme. The speed of evolution is slow and easy to fall into local minima. In this paper, we propose a fast sweeping optimization algorithm to minimize global cosine fitting (GCF) model. When moving a pixel from the one side region to the another side region of evolving contour, the sweeping algorithm calculates the energy change directly and checks whether the cosine fitting energy is decreased. With this, we can avoid solving the Euler-Lagrange equation and the partial differential equation, which usually take a lot of time. Moreover, our proposal is robust to initial level set contour and it automatically handles the topological variety, algorithm automatic termination and no longer requires the reinitialization step, parameter adjustment and the distance regularization term. The experiments on real noise and synthetic images show the effectiveness of the sweeping algorithm.

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Acknowledgements

The authors would like to express their thanks to the referees for their valuable suggestions. This work was supported by the grant from the National Natural Science Foundation of China, Nos. 61672204 and 61806068, the Natural Science Foundation of Anhui Provincial, No. 1908085MF184, 1908085QF285, in part by the Key Research Plan of Anhui Province, No. 201904d07020002.

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Authors and Affiliations

  1. School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, Anhui, China

    Yanping Chen, Le Zou & Xiaofeng Wang

  2. Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei, 230601, Anhui, China

    Zhize Wu

  3. College of Bioengineering Food and Environmental Sciences, Hefei University, Hefei, 23060, Anhui, China

    Qianjing Huang

Authors
  1. Yanping Chen
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  2. Le Zou
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  3. Zhize Wu
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  4. Qianjing Huang
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  5. Xiaofeng Wang
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Corresponding author

Correspondence to Xiaofeng Wang .

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Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Li Ning

  2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Vincent Chau

  3. The University of Hong Kong, Hong Kong, China

    Francis Lau

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Chen, Y., Zou, L., Wu, Z., Huang, Q., Wang, X. (2021). A Fast Algorithm for Image Segmentation Based on Global Cosine Fitting Energy Model. In: Ning, L., Chau, V., Lau, F. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2020. Communications in Computer and Information Science, vol 1362. Springer, Singapore. https://doi.org/10.1007/978-981-16-0010-4_17

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  • DOI: https://doi.org/10.1007/978-981-16-0010-4_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0009-8

  • Online ISBN: 978-981-16-0010-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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