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Edge Detection for Cement Images Based on Interactive Genetic Algorithm

  • Guangyue Gao
  • Lin WangEmail author
  • Bo YangEmail author
  • Liangliang Zhang
  • Fengyang Sun
  • Ajith Abraham
  • Shuangrong Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)

Abstract

The cement is a type of cementious material which hydration is an extremely complex process. In order to research the evolution of particles during cement hydration, the particles should be differentiated from cement images. However, the existence of partial volume effect and similarity of intensity between different phases causes the boundaries of particles are not clear. Therefore, it is difficult for the traditional edge detection methods to differentiate the edges of the particles from cement microstructural images. In this paper, a method detecting edges for cement image based on interactive genetic algorithm (IGA) is proposed. The IGA utilizes human knowledge to evaluate the quality of evolved convolution templates to yield a better detector. Experimental results show that the method can accurately detect the edge for cement images.

Notes

Acknowledgment

This work was supported by National Natural Science Foundation of China under Grant No. 61573166, No. 61572230, No. 81671785, No. 61373054, No. 61472164, No. 61472163, No. 61672262, No. 61640218. Shandong Provincial Natural Science Foundation, China, under Grant ZR2015JL025, ZR2014JL042. Science and technology project of Shandong Province under Grant No. 2015GGX101025. Project of Shandong Province Higher Educational Science and Technology Program under Grant no. J16LN07. Shandong Provincial Key R&D Program under Grant No. 2016ZDJS01A12, No.2016GGX101001.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Guangyue Gao
    • 1
  • Lin Wang
    • 1
    • 2
    Email author
  • Bo Yang
    • 1
    • 2
    Email author
  • Liangliang Zhang
    • 1
  • Fengyang Sun
    • 1
  • Ajith Abraham
    • 3
  • Shuangrong Liu
    • 1
  1. 1.Shandong Provincial Key Laboratory of Network based Intelligent ComputingUniversity of JinanJinanChina
  2. 2.School of InformaticsLinyi UniversityLinyiChina
  3. 3.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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