Skip to main content
SpringerLink
Log in

A new kernel development algorithm for edge detection using singular value ratios

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

Abstract

The perceptual quality of an image is very sensitive to the degradation of the edge information which is usually caused by many video signal applications such as super-resolution and denoising. Hence, it is very important to detect and enhance the edge information of the image. In this research work, new sets of kernels for edge detection using ratios of singular values of an image are proposed, which results in more detailed detection of edges in the original image. The parameters, which are the elements of kernel matrices and the threshold value used for producing binary image after convolving the kernels with the image of the proposed method, are optimised to achieve more detailed edge detection of the image. The experimental results show that more detailed edges are detected by the proposed method compared to the conventional edge detection techniques.

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

Access this article

Log in via an institution

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
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)

    Article  Google Scholar 

  2. Ari, S., Ghosh, D.K., Mohanty, P.K.: Edge detection using ACO and F ratio. SIViP 8(4), 625–634 (2014)

    Article  Google Scholar 

  3. Chen, W., Tian, Q., Liu, J., Wang, Q.: Nonlocal low-rank matrix completion for image interpolation using edge detection and neural network. SIViP 8(4), 657–663 (2014)

    Article  Google Scholar 

  4. Anbarjafari, G., Ozcinar, C.: Imperceptible non-blind watermarking and robustness against tone mapping operation attacks for high dynamic range images. Multimed. Tools Appl. 1–15 (2018)

  5. Dollár, P., Zitnick, C.L. : Structured forests for fast edge detection. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1841–1848. IEEE (2013)

  6. Dinh, C.V., Leitner, R., Paclik, P., Loog, M., Duin, R.P.: SEDMI: saliency based edge detection in multispectral images. Image Vis. Comput. 29(8), 546–556 (2011)

    Article  Google Scholar 

  7. Pan, X., Ye, Y., Cheng, J., Wang, D., Jiang, B.: Composite derivative and edge detection. SIViP 8(3), 523–531 (2014)

    Article  Google Scholar 

  8. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  9. Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by Helmholtz principle. J. Math. Imaging Vis. 14(3), 271–284 (2001)

    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 (2015)

    Article  Google Scholar 

  11. Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B Biol. Sci. 207(1167), 187–217 (1980)

    Article  Google Scholar 

  12. Romero-Manchado, A., Rojas-Sola, J.I.: Application of gradient-based edge detectors to determine vanishing points in monoscopic images: comparative study. Image Vis. Comput. 43, 1–15 (2015)

    Article  Google Scholar 

  13. Maini, R., Aggarwal, H.: Study and comparison of various image edge detection techniques. Int. J. Image Process. (IJIP) 3(1), 1–11 (2009)

    Google Scholar 

  14. Shrivakshan, G., Chandrasekar, C.: A comparison of various edge detection techniques used in image processing. Int. J. Comput. Sci. Issues (IJCSI) 9(5), 272–276 (2012)

    Google Scholar 

  15. Tarvas, K., Bolotnikova, A., Anbarjafari, G.: Edge information based object classification for NAO robots. Cogent Eng. 3(1), 1262571 (2016)

    Article  Google Scholar 

  16. Canny, J.F.: Finding edges and lines in images. Tech. Rep., DTIC Document (1983)

  17. 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)

    Article  MathSciNet  MATH  Google Scholar 

  18. Fleck, M.M.: Some defects in finite-difference edge finders. IEEE Trans. Pattern Anal. Mach. Intell. 3, 337–345 (1992)

    Article  Google Scholar 

  19. Boie, R.A., Cox, I., Rehak, P.: On optimum edge recognition using matched filters. In: IEEE Conference on Computer Vision and Pattern Recognition. Proceedings, pp. 100–108. IEEE (1986)

  20. Boise, R., Cox, I.J.: Two dimensional optimum edge recognition using matched and Wiener filters for machine vision. In: Proceedings of International Conference on Computer Vision, pp. 1–4 (1987)

  21. Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recognit. 46(3), 1020–1038 (2013)

    Article  Google Scholar 

  22. Kim, D.-S., Lee, W.-H., Kweon, I.-S.: Automatic edge detection using 3 \(\times \) 3 ideal binary pixel patterns and fuzzy-based edge thresholding. Pattern Recognit. Lett. 25(1), 101–106 (2004)

    Article  Google Scholar 

  23. Bhandarkar, S.M., Zhang, Y., Potter, W.D.: An edge detection technique using genetic algorithm-based optimization. Pattern Recognit. 27(9), 1159–1180 (1994)

    Article  Google Scholar 

  24. Jin-Yu, Z., Yan, C., Xian-Xiang, H.: Edge detection of images based on improved Sobel operator and genetic algorithms. In: International Conference on Image Analysis and Signal Processing, 2009. IASP 2009, pp. 31–35. IEEE (2009)

  25. Srinivasan, V., Bhatia, P., Ong, S.H.: Edge detection using a neural network. Pattern Recognit. 27(12), 1653–1662 (1994)

    Article  Google Scholar 

  26. Li, H., Liao, X., Li, C., Huang, H., Li, C.: Edge detection of noisy images based on cellular neural networks. Commun. Nonlinear Sci. Numer. Simul. 16(9), 3746–3759 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  27. Liao, S., Jain, A.K., Li, S.Z.: A fast and accurate unconstrained face detector. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 211–223 (2016)

    Article  Google Scholar 

  28. Cumani, A.: Edge detection in multispectral images. CVGIP Graph. Models Image Process. 53(1), 40–51 (1991)

    Article  MATH  Google Scholar 

  29. Wang, Y., Teoh, E.K.: Object contour extraction using adaptive B-snake model. J. Math. Imaging Vis. 24(3), 295–306 (2006)

    Article  MathSciNet  Google Scholar 

  30. Amstutz, S., Fehrenbach, J.: Edge detection using topological gradients: a scale-space approach. J. Math. Imaging Vis. 52(2), 249–266 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  31. Haamer, R.E., Kulkarni, K., Imanpour, N., Haque , M.A., Avots, E., Breisch, M., Nasrollahi, K., Guerrero, S.E., Ozcinar, C., Baro, X., et al.: Changes in facial expression as biometric: a database and benchmarks of identification. In: IEEE Conf. on Automatic Face and Gesture Recognition Workshops. IEEE (2018)

  32. De Lathauwer, L., De Moor, B., Vandewalle, J.: B.S.S. by Higher-Order, “Singular value decomposition”. In: Proc. EUSIPCO-94, Edinburgh, Scotland, UK, vol. 1, pp. 175–178 (1994)

  33. Demirel, H., Anbarjafari, G., Jahromi, M.N.S.: Image equalization based on singular value decomposition. In: 23rd International Symposium on Computer and Information Sciences, 2008. ISCIS’08, pp. 1–5. IEEE (2008)

  34. Ozcinar, C., Demirel, H., Anbarjafari, G.: Image equalization using singular value decomposition and discrete wavelet transform. In: Discrete Wavelet Transforms-Theory and Applications. InTech (2011)

  35. Demirel, H., Anbarjafari, G., Ozcinar, C., Izadpanahi, S.: Video resolution enhancement by using complex wavelet transform. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 2093–2096. IEEE (2011)

  36. Nalwa, V.S., Binford, T.O.: On detecting edges. IEEE Trans. Pattern Anal. Mach. Intell. 6, 699–714 (1986)

    Article  Google Scholar 

  37. Lee, J.S., Haralick, R.M., Shapiro, L.G.: Morphologic edge detection. IEEE J. Robot. Autom. 3(2), 142–156 (1987)

    Article  Google Scholar 

  38. Wilson, R., Bhalerao, A.: Kernel designs for efficient multiresolution edge detection and orientation estimation. IEEE Trans. Pattern Anal. Mach. Intell. 3, 384–390 (1992)

    Article  Google Scholar 

  39. Elder, J.H., Zucker, S.W.: Local scale control for edge detection and blur estimation. IEEE Trans. Pattern Anal. Mach. Intell. 20(7), 699–716 (1998)

    Article  Google Scholar 

  40. 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 

  41. Madabusi, S., Gangashetty, S.V.: Edge detection for facial images under noisy conditions. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 2689–2693. IEEE (2012)

  42. Jose, A., Seelamantula, C.S.: Bilateral edge detectors. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1449–1453. IEEE (2013)

  43. Cisar, P., Cisar, S.M., Markoski, B.: Kernel sets in compass edge detection. In: 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 239–242. IEEE (2013)

  44. Qiu, C., Wu, J.: A new method for edge detection in digital images. In: 2013 Ninth International Conference on Natural Computation (ICNC), pp. 1234–1238. IEEE (2013)

  45. Zhang, W.-C., Shui, P.-L.: Contour-based corner detection via angle difference of principal directions of anisotropic gaussian directional derivatives. Pattern Recognit. 48(9), 2785–2797 (2015)

    Article  Google Scholar 

  46. Weber, A.G.: The USC-SIPI Image Database. Tech. Rep., University of Southern California, Signal and Image Processing Institute, Department of Electrical Engineering, Los Angeles, CA 90089-2564 USA, 3740 McClintock Ave (1997)

  47. Tanchenko, A.: Visual-psnr measure of image quality. J. Vis. Commun. Image Represent. 25(5), 874–878 (2014)

    Article  Google Scholar 

  48. Baker, S., Nayar, S.: Global measures of coherence for edge detector evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 373–379 (1999)

  49. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 898–916 (2011)

    Article  Google Scholar 

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

  51. Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125, 1–16 (2017)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. iCV Lab, Institute of Technology, University of Tartu, Tartu, Estonia

    Egils Avots, Hasan Said Arslan, Lembit Valgma, Jelena Gorbova & Gholamreza Anbarjafari

  2. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Hasan Said Arslan

  3. Department of Electrical and Electronic Engineering, Hasan Kalyoncu University, Gaziantep, Turkey

    Gholamreza Anbarjafari

Authors
  1. Egils Avots
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Said Arslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lembit Valgma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jelena Gorbova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gholamreza Anbarjafari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gholamreza Anbarjafari.

Additional information

This work has been partially supported by Estonian Information Technology Foundation, Skype Technologies and Estonian Research Council Grant (PUT638), the Scientific and Technological Research Council of Turkey (TÜBÏTAK) 1001 Project (116E097), and the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Avots, E., Arslan, H.S., Valgma, L. et al. A new kernel development algorithm for edge detection using singular value ratios. SIViP 12, 1301–1309 (2018). https://doi.org/10.1007/s11760-018-1283-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-018-1283-z

Keywords

Search

Navigation