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Edge Detection Based on the Fusion of Multiscale Anisotropic Edge Strength Measurements

  • Gang WangEmail author
  • Bernard De Baets
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)

Abstract

Edge detection plays an essential role in many computer vision tasks, but there is limited literature on the fusion of multiscale edge strength measurements. In this paper, we extend an edge detector using both isotropic and anisotropic Gaussian kernels in multiscale space to obtain the multiscale anisotropic edge strength measurements (AESMs). Subsequently, we propose a fusion scheme of multiscale AESMs based on geometric mean. This scheme inherits the merits of the isotropic/anisotropic Gaussian kernel based method and suppress the scale-space diffusion at the same time. Experimental results on example images in the EUSFLAT Edge Detection Competition dataset illustrate that the proposed method outperforms the widely used Canny method and the state-of-the-art isotropic/anisotropic Gaussian kernel method.

Keywords

Edge detection Anisotropic gaussian kernel Multiscale edge measurement fusion 

References

  1. 1.
    Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)CrossRefGoogle Scholar
  2. 2.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  3. 3.
    Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2014)CrossRefGoogle Scholar
  4. 4.
    Li, Y., Wang, S., Tian, Q., Ding, X.: A survey of recent advances in visual feature detection. Neurocomputing 149, 736–751 (2015)CrossRefGoogle Scholar
  5. 5.
    Lopez-Molina, C., De Baets, B., Bustince, H., Sanz, J., Barrenechea, E.: Multiscale edge detection based on Gaussian smoothing and edge tracking. Knowl.-Based Syst. 44, 101–111 (2013)CrossRefGoogle Scholar
  6. 6.
    Lopez-Molina, C., Vidal-Diez De Ulzurrun, G., Baetens, J.M., Van Den Bulcke, J., De Baets, B.: Unsupervised ridge detection using second order anisotropic Gaussian kernels. Sig. Process. 116, 55–67 (2015)CrossRefGoogle Scholar
  7. 7.
    Perfilieva, I., Hodáková, P., Hurtík, P.: Differentiation by the F-transform and application to edge detection. Fuzzy Sets Syst. 288, 96–114 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Rosenfeld, A., Thurston, M.: Edge and curve detection for visual scene analysis. IEEE Trans. Comput. C–20(5), 562–569 (1971)CrossRefGoogle Scholar
  9. 9.
    Shui, P.L., Zhang, W.C.: Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels. Pattern Recogn. 45(2), 806–820 (2012)CrossRefzbMATHGoogle Scholar
  10. 10.
    Zhang, W., Zhao, Y., Breckon, T.P., Chen, L.: Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recogn. 63, 193–205 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.KERMIT, Research Unit Knowledge-based SystemsGhent UniversityGhentBelgium

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