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Improved Canny Edge Detector Using Principal Curvatures

Received: 30 June 2020     Accepted: 20 July 2020     Published: 10 August 2020
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Abstract

Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms. It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect “true” edges, while suppressing “false” non-edge filter responses. Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex. This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification. Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny’s algorithm.

Published in Journal of Electrical and Electronic Engineering (Volume 8, Issue 4)
DOI 10.11648/j.jeee.20200804.11
Page(s) 109-116
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Edge Detection, Non-maximum Suppression, Canny Edge Detector, Low-Level Processing

References
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[16] A. Neubeck and L. Van Gool, “Efficient non-maximum suppression,” in International Conference on Pattern Recognition, 2006, vol. 3, pp. 850-855.
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[19] C. Sun and P. Vallotton, “Fast linear feature detection using multiple directional non-maximum suppression,” J. Microsc., vol. 234, no. 2, pp. 147-157, 2009.
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Cite This Article
  • APA Style

    Cesar Bustacara-Medina, Leonardo Florez-Valencia, Luis Carlos Diaz. (2020). Improved Canny Edge Detector Using Principal Curvatures. Journal of Electrical and Electronic Engineering, 8(4), 109-116. https://doi.org/10.11648/j.jeee.20200804.11

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    ACS Style

    Cesar Bustacara-Medina; Leonardo Florez-Valencia; Luis Carlos Diaz. Improved Canny Edge Detector Using Principal Curvatures. J. Electr. Electron. Eng. 2020, 8(4), 109-116. doi: 10.11648/j.jeee.20200804.11

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    AMA Style

    Cesar Bustacara-Medina, Leonardo Florez-Valencia, Luis Carlos Diaz. Improved Canny Edge Detector Using Principal Curvatures. J Electr Electron Eng. 2020;8(4):109-116. doi: 10.11648/j.jeee.20200804.11

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  • @article{10.11648/j.jeee.20200804.11,
      author = {Cesar Bustacara-Medina and Leonardo Florez-Valencia and Luis Carlos Diaz},
      title = {Improved Canny Edge Detector Using Principal Curvatures},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {8},
      number = {4},
      pages = {109-116},
      doi = {10.11648/j.jeee.20200804.11},
      url = {https://doi.org/10.11648/j.jeee.20200804.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20200804.11},
      abstract = {Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms. It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect “true” edges, while suppressing “false” non-edge filter responses. Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex. This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification. Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny’s algorithm.},
     year = {2020}
    }
    

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    T1  - Improved Canny Edge Detector Using Principal Curvatures
    AU  - Cesar Bustacara-Medina
    AU  - Leonardo Florez-Valencia
    AU  - Luis Carlos Diaz
    Y1  - 2020/08/10
    PY  - 2020
    N1  - https://doi.org/10.11648/j.jeee.20200804.11
    DO  - 10.11648/j.jeee.20200804.11
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 109
    EP  - 116
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20200804.11
    AB  - Canny edge detector is a very popular and effective edge feature detector that is used as a preprocessing step in many computer vision algorithms. It is a multi-step detector, which performs smoothing, filtering, non-maximum suppression, followed by a connected-component analysis stage to detect “true” edges, while suppressing “false” non-edge filter responses. Based on the literature, traditional Canny edge detector is sensitive to noise, hence it may lose the weak edge information after noise removal and show poor adaptability of fixed parameters like threshold values. In addition, Canny algorithm tends to over-smooth the noise, resulting in the loss of edge images or pseudo-edges, and the method of selecting thresholds is artificial, and the subjective factors are strong and computationally complex. This paper proposes an improvement to the traditional Canny algorithm by adding curvature information in the non-maximum suppression step (NMS) in order to obtain an accurate edge identification. Additionally, a set of tests and results is presented that show how by adding curvature characteristics to the NMS process, better results are obtained in the edge detection in Canny’s algorithm.
    VL  - 8
    IS  - 4
    ER  - 

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Author Information
  • Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia

  • Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia

  • Department of Systems Engineering, Pontificia Universidad Javeriana, Bogota D. C., Colombia

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