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Image Edge Detection Method Based on Ant Colony Algorithm

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

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Abstract

Ant colony algorithm has good results in finding the optimal solution in a certain field; and image edge detection is an essential foundation for all kinds of image processing. How to improve image edge detection becomes a hot topic in image processing. In this paper, the ant colony algorithm is applied to image edge detection, and the ant colony algorithm’s discreteness, parallelism and positive feedback are fully utilized. Through repeated iteration, pheromone acquisition and pheromone matrix were continuously updated to search for images step by step. The experimental results show that the ant colony algorithm can effectively detect the edge of the image, and the detection effect of the algorithm is significantly improved compared with the Roberts algorithm.

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Authors and Affiliations

  1. Modern Education Technology Center, Guilin University of Technology, Guangxi, 541004, China

    Qirong Lu

  2. College of Mechanical and Control Engineering, Guilin University of Technology, Guangxi, 541004, China

    Qianmin Liang, Jiqiu Chen & Jiwei Xia

Authors
  1. Qirong Lu
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  2. Qianmin Liang
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  3. Jiqiu Chen
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  4. Jiwei Xia
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Corresponding author

Correspondence to Qianmin Liang .

Editor information

Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Lu, Q., Liang, Q., Chen, J., Xia, J. (2019). Image Edge Detection Method Based on Ant Colony Algorithm. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_14

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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