Skip to main content

Advertisement

Springer Nature Link
Log in

Edge detection using multi-directional anisotropic Gaussian directional derivative

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Edge detection is a crucial task for computer vision. In this paper, we propose to use both the multi-directional first-order anisotropic Gaussian derivative and the second-order anisotropic Gaussian derivative to extract image gray information. The first-order derivative is utilized to determine the gradient direction, while the second-order derivative is used to identify the gradient magnitude. By double filtering of the feature information, the operator’s robustness is improved, and the edge stretching is reduced. The multi-directional filters can obtain enough gradient information to avoid edge missing. Moreover, we propose to use the adaptive thresholds to improve the operator’s generalizability. The aggregate receiver operating characteristic curve shows that the proposed method improves the accuracy of edge detection and exhibits strong robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Lyu, L., Higgins, G., Zhu, W.: Operational modal analysis of a rotating structure using image-based tracking continuously scanning laser doppler vibrometry via a novel edge detection method. J. Sound Vib. 525, 116797 (2022)

    Article  Google Scholar 

  2. Yang, C., Wang, W., Feng, X.: Joint image restoration and edge detection in cooperative game formulation. Signal Process. 191, 108363 (2022)

    Article  Google Scholar 

  3. Babu, P. A., Sridhar, P., Vallabhuni, R. R.: “Fake currency recognition system using edge detection,” in 2022 Interdisciplinary Research in Technology and Management (IRTM), pp. 1–5, IEEE, (2022)

  4. Li, B., Qiu, S., Jiang, W., Zhang, W., Le, M.: A uav detection and tracking algorithm based on image feature super-resolution. Wirel. Commun. Mobile Comput., 2022 (2022)

  5. Li, K., He, F.-Z., Yu, H.-P.: Robust visual tracking based on convolutional features with illumination and occlusion handing. J. Comput. Sci. Technol. 33(1), 223–236 (2018)

    Article  Google Scholar 

  6. Ismail, S.M., Said, L.A., Radwan, A.G., Madian, A.H., Abu-ElYazeed, M.F.: A novel image encryption system merging fractional-order edge detection and generalized chaotic maps. Signal Process. 167, 107280 (2020)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Song, R., Zhang, Z., Liu, H.: Edge connection based canny edge detection algorithm. Pattern Recognit. Image Anal. 27(4), 740–747 (2017)

    Article  Google Scholar 

  9. Liu, Y., Xie, Z., Liu, H.: An adaptive and robust edge detection method based on edge proportion statistics. IEEE Trans. Image Process. 29, 5206–5215 (2020)

    Article  MATH  Google Scholar 

  10. Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2014)

    Article  Google Scholar 

  11. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2010)

    Article  Google Scholar 

  12. Lim, J.J., Zitnick, C.L., Dollár, P.: Sketch tokens: a learned mid-level representation for contour and object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3158–3165 (2013)

  13. Zhang, Z., Xing, F., Shi, X., Yang, L.: Semicontour: a semi-supervised learning approach for contour detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 251–259 (2016)

  14. Liu, Y., Cheng, M.-M., Hu, X., Wang, K., Bai, X.: Richer convolutional features for edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3000–3009 (2017)

  15. He, J., Zhang, S., Yang, M., Shan, Y, Huang, T.: Bi-directional cascade network for perceptual edge detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3828–3837 (2019)

  16. Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietikäinen, M., Liu, L.: Pixel difference networks for efficient edge detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5117–5127 (2021)

  17. Soria, X., Sappa, A., Humanante, P., Akbarinia, A.: Dense extreme inception network for edge detection. Pattern Recognit. 139, 109461 (2023)

    Article  Google Scholar 

  18. Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)

  19. Pu, M., Huang, Y., Liu, Y., Guan, Q., Ling, H.: Edter: edge detection with transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1402–1412 (2022)

  20. Gao, Y., Tang, C., Lang, J., Lv, J.: End-to-end edge detection via improved transformer model. In: Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part IV 28, pp. 514–525. SpringerZ (2021)

  21. Elharrouss, O., Hmamouche, Y., Idrissi, A.K., El Khamlichi, B., El Fallah-Seghrouchni, A.: Refined edge detection with cascaded and high-resolution convolutional network. Pattern Recognit. 138, 109361 (2023)

    Article  Google Scholar 

  22. Liu, Y., Cheng, M.-M., Fan, D.-P., Zhang, L., Bian, J.-W., Tao, D.: Semantic edge detection with diverse deep supervision. Int. J. Comput. Vis. 130(1), 179–198 (2022)

    Article  Google Scholar 

  23. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  24. Torre, V., Poggio, T.A.: On edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 2, 147–163 (1986)

    Article  Google Scholar 

  25. You, N., Han, L., Zhu, D., Song, W.: Research on image denoising in edge detection based on wavelet transform. Appl. Sci. 13(3), 1837 (2023)

    Article  Google Scholar 

  26. Anand, S., Nagajothi,K., Nithya, K.: Edge detection using stationary wavelet transform, hmm, and em algorithm. arXiv:2004.11296 (2020)

  27. Hou, S.-M., Jia, C.-L., Wanga, Y.-B., Brown, M.: A review of the edge detection technology. Sparklinglight Trans. Artif. Intell. Quantum Comput. (STAIQC) 1(2), 26–37 (2021)

    Article  Google Scholar 

  28. Shui, P.-L., Zhang, W.-C.: Noise-robust edge detector combining isotropic and anisotropic Gaussian kernels. Pattern Recognit. 45(2), 806–820 (2012)

    Article  MATH  Google Scholar 

  29. Liu, S.-L., Niu, Z.-D., Sun, G., Chen, Z.-P.: Gabor filter-based edge detection: a note. Optik 125(15), 4120–4123 (2014)

    Article  Google Scholar 

  30. Rakesh, R.R., Chaudhuri, P., Murthy, C.: Thresholding in edge detection: a statistical approach. IEEE Trans. Image Process. 13(7), 927–936 (2004)

    Article  Google Scholar 

  31. Shui, P.-L., Wang, F.-P.: Anti-impulse-noise edge detection via anisotropic morphological directional derivatives. IEEE Trans. Image Process. 26(10), 4962–4977 (2017)

  32. Nezhadarya, E., Ward, R.K.: A new scheme for robust gradient vector estimation in color images. IEEE Trans. Image Process. 20(8), 2211–2220 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  33. Akinlar, C., Topal, C.: Colored: color edge and segment detection by edge drawing (ed). J. Vis. Commun. Image Represent. 44, 82–94 (2017)

    Article  Google Scholar 

  34. Bowyer, K., Kranenburg, C., Dougherty, S.: Edge detector evaluation using empirical roc curves. Comput. Vis. Image Underst. 84(1), 77–103 (2001)

Download references

Funding

Funding was provided by National Natural Science Foundation of China (No. 62176204), Innovation Capability Support Program of Shaanxi (No. 2021TD-29), Youth Innovation Team of Shaanxi Universities, Shaanxi Province Qin Chuangyuan "scientists+ engineers"team construction (No:2023KXJ-061).

Author information

Author notes

  1. Junfeng Jing and Weichuan Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Electronic Information, Xi’an Polytechnic University, No.19 Jinhua South Road, Xi’an, 710048, China

    Ying An & Junfeng Jing

  2. Xi’an Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi’an, 710048, China

    Junfeng Jing

  3. CSIRO Data61, PO Box 76, Epping, NSW, 1710, Australia

    Weichuan Zhang

Authors
  1. Ying An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junfeng Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weichuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Junfeng Jing.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 827 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, Y., Jing, J. & Zhang, W. Edge detection using multi-directional anisotropic Gaussian directional derivative. SIViP 17, 3767–3774 (2023). https://doi.org/10.1007/s11760-023-02604-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02604-0

Keywords