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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2014: Simulated Evolution and Learning pp 810–821Cite as

Automatic Resolution Selection for Edge Detection Using Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

Abstract

When Genetic Programming is applied to edge detection, the computational cost is generally expensive. When a set of natural images are used to train edge detectors, using their high resolutions is more expensive than using their low resolutions. However, from existing reports, it is hard to find the influence on performance from using different sampling techniques on low resolutions. In this paper, we propose a GP system to automatically select the resolutions of a single training image to train edge detectors. The results of the experiments show that the GP system can effectively evolve edge detectors based on automatic resolution selection.

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Author information

Authors and Affiliations

  1. School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Wenlong Fu & Mark Johnston

  2. School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    Mengjie Zhang

Authors
  1. Wenlong Fu
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  2. Mark Johnston
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  3. Mengjie Zhang
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Fu, W., Johnston, M., Zhang, M. (2014). Automatic Resolution Selection for Edge Detection Using Genetic Programming. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_68

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_68

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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