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Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1657–1665 | Cite as

Contour detection based on anisotropic edge strength and hierarchical superpixel contrast

  • Gang WangEmail author
  • Bernard De Baets
Original Paper
  • 143 Downloads

Abstract

Contour detection is a fundamental problem in computer vision, yet existing methods usually suffer from the interference of noise and textures. To address this problem, we present an unsupervised contour detection method based on anisotropic edge strength and hierarchical superpixel contrast. The anisotropic edge strength is obtained through the first derivative of anisotropic Gaussian kernels which incorporates an adaptive anisotropy factor. The anisotropic kernel improves the robustness to noise, while the adaptive anisotropic factor attenuates the anisotropy stretch effect. Using a method based on region merging, we obtain a hierarchical set of superpixel maps and thus compute superpixel contrast maps at different hierarchy levels. Consequently, the contour strength map is obtained by multiplying the anisotropic edge strength map by the average of the hierarchical superpixel contrast maps. Experimental results on two publicly available datasets validate the superiority of the proposed method over the competing methods. On the Berkeley Segmentation Dataset & Benchmark 300 and the Berkeley Segmentation Dataset & Benchmark 500, our method obtains (optimal dataset scale) F-measure values of 0.63 and 0.67, respectively, an improvement of at least 0.06 over the competing methods.

Keywords

Contour detection Edge strength Anisotropic Gaussian kernel Superpixel segmentation Hierarchical superpixel contrast 

Notes

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)CrossRefGoogle Scholar
  3. 3.
    Avots, E., Arslan, H.S., Valgma, L., Gorbova, J., Anbarjafari, G.: A new kernel development algorithm for edge detection using singular value ratios. Signal Image Video Process. 12(7), 1301–1309 (2018)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  7. 7.
    Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)CrossRefGoogle Scholar
  8. 8.
    El Jaafari, I., El Ansari, M., Koutti, L.: Fast edge-based stereo matching approach for road applications. Signal Image Video Process. 11(2), 267–274 (2017)CrossRefGoogle Scholar
  9. 9.
    Fang, L., Li, S., Kang, X., Benediktsson, J.A.: Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model. IEEE Trans. Geosci. Remote Sensing 53(8), 4186–4201 (2015)CrossRefGoogle Scholar
  10. 10.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  11. 11.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Trans. Image Process. 12(7), 729–739 (2003)Google Scholar
  12. 12.
    Hu, Z., Wu, Z., Zhang, Q., Fan, Q., Xu, J.: A spatially-constrained color-texture model for hierarchical VHR image segmentation. IEEE Geosci. Remote Sens. Lett. 10(1), 120–124 (2013)CrossRefGoogle Scholar
  13. 13.
    Koschan, A., Abidi, M.: Detection and classification of edges in color images. IEEE Signal Process. Mag. 22(1), 64–73 (2005)CrossRefGoogle Scholar
  14. 14.
    Levinshtein, A., Sminchisescu, C., Dickinson, S.: Optimal contour closure by superpixel grouping. In: Proceedings of the European Conference on Computer Vision, pp. 480–493 (2010)CrossRefGoogle Scholar
  15. 15.
    Li, Y., Wang, S., Tian, Q., Ding, X.: A survey of recent advances in visual feature detection. Neurocomputing 149, 736–751 (2015)CrossRefGoogle Scholar
  16. 16.
    Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998)CrossRefGoogle Scholar
  17. 17.
    Lopez-Molina, C., De Baets, B., Bustince, H.: Quantitative error measures for edge detection. Pattern Recognit. 46(4), 1125–1139 (2013)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)Google Scholar
  20. 20.
    Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar
  21. 21.
    Mun, J., Jang, Y., Kim, J.: Propagated guided image filtering for edge-preserving smoothing. Signal Image Video Process. 12(6), 1165–1172 (2018)CrossRefGoogle Scholar
  22. 22.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  23. 23.
    Rosenfeld, A., Thurston, M.: Edge and curve detection for visual scene analysis. IEEE Trans. Comput. C–20(5), 562–569 (1971)CrossRefGoogle Scholar
  24. 24.
    Shui, P., Wang, F.: Anti-impulse-noise edge detection via anisotropic morphological directional derivatives. IEEE Trans. Image Process. 26(10), 4962–4977 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Shui, P., Zhang, W.: Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels. Pattern Recognit. 45(2), 806–820 (2012)CrossRefGoogle Scholar
  26. 26.
    Sobel, I.: Camera models and machine perception. Ph.D. thesis, Stanford University (1970)Google Scholar
  27. 27.
    Stutz, D., Hermans, A., Leibe, B.: Superpixels: an evaluation of the state-of-the-art. Comput. Vis. Image Underst. 166, 1–27 (2018)CrossRefGoogle Scholar
  28. 28.
    Wang, F., Shui, P.: Noise-robust color edge detector using gradient matrix and anisotropic Gaussian directional derivative matrix. Pattern Recognit. 52, 346–357 (2016)CrossRefGoogle Scholar
  29. 29.
    Wang, G., De Baets, B.: Edge detection based on the fusion of multiscale anisotropic edge strength measurements. In: Proceedings of the Conference of the European Society for Fuzzy Logic and Technology, vol. 3, pp. 530–536 (2017)Google Scholar
  30. 30.
    Wang, G., Lopez-Molina, C., De Baets, B.: Blob reconstruction using unilateral second order Gaussian kernels with application to high-ISO long-exposure image denoising. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4817–4825 (2017)Google Scholar
  31. 31.
    Wang, G., Lopez-Molina, C., de Vidal-Diez Ulzurrun, G., De Baets, B.: Noise-robust line detection using normalized and adaptive second-order anisotropic Gaussian kernels. Signal Process. 160, 252–262 (2019)CrossRefGoogle Scholar
  32. 32.
    Wei, X., Yang, Q., Gong, Y., Ahuja, N., Yang, M.: Superpixel hierarchy. IEEE Trans. Image Process. 27(10), 4838–4849 (2018)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125, 3–18 (2017)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Xu, Q., Varadarajan, S., Chakrabarti, C., Karam, L.J.: A distributed Canny edge detector: algorithm and FPGA implementation. IEEE Trans. Image Process. 23(7), 2944–2960 (2014)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Yang, K., Li, C., Li, Y.: Multifeature-based surround inhibition improves contour detection in natural images. IEEE Trans. Image Process. 23(12), 5020–5032 (2014)MathSciNetCrossRefGoogle Scholar
  36. 36.
    You, X., Du, L., Cheung, Ym, Chen, Q.: A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans. Image Process. 19(12), 3271–3284 (2010)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)CrossRefGoogle Scholar
  38. 38.
    Zhang, W., Zhao, Y., Breckon, T.P., Chen, L.: Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognit. 63, 193–205 (2017)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.KERMIT, Department of Data Analysis and Mathematical ModellingGhent UniversityGhentBelgium

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