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Application of a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in Image Processing

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Abstract

A novel fuzzy clustering method based on chaos immune evolutionary algorithm (CIEFCM) is presented to solve fuzzy edge detection problems in image processing. In CIEFCM, a tiny disturbance is added to a filial generation group using a chaos variable and the disturbance amplitude is adjusted step by step, which greatly improves the colony diversity of the immune evolution algorithm (IEA). The experimental results show that the method not only can correctly detect the fuzzy edge and exiguous edge but can evidently improve the searching efficiency of fuzzy clustering algorithm based on IEA.

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Authors and Affiliations

  1. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Wang Sun-an & Guo Zi-long

Authors
  1. Wang Sun-an
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  2. Guo Zi-long
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Correspondence to Wang Sun-an.

Additional information

Translated from Journal of Xi’an Jiaotong University, 2004, 38(7) (in Chinese)

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Wang, Sa., Guo, Zl. Application of a Novel Fuzzy Clustering Method Based on Chaos Immune Evolutionary Algorithm for Edge Detection in Image Processing. Front. Mech. Eng. China 1, 85–89 (2006). https://doi.org/10.1007/s11465-005-0023-6

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  • DOI: https://doi.org/10.1007/s11465-005-0023-6

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