Signal, Image and Video Processing

, Volume 12, Issue 7, pp 1301–1309 | Cite as

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

  • Egils Avots
  • Hasan Said Arslan
  • Lembit Valgma
  • Jelena Gorbova
  • Gholamreza Anbarjafari
Original Paper


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.


Edge detection Singular value decomposition Edge kernel Thresholding Segmentation 


  1. 1.
    Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)CrossRefGoogle Scholar
  2. 2.
    Ari, S., Ghosh, D.K., Mohanty, P.K.: Edge detection using ACO and F ratio. SIViP 8(4), 625–634 (2014)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)Google Scholar
  5. 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)Google Scholar
  6. 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)CrossRefGoogle Scholar
  7. 7.
    Pan, X., Ye, Y., Cheng, J., Wang, D., Jiang, B.: Composite derivative and edge detection. SIViP 8(3), 523–531 (2014)CrossRefGoogle Scholar
  8. 8.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)CrossRefGoogle Scholar
  9. 9.
    Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by Helmholtz principle. J. Math. Imaging Vis. 14(3), 271–284 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)CrossRefGoogle Scholar
  11. 11.
    Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B Biol. Sci. 207(1167), 187–217 (1980)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 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. 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. 15.
    Tarvas, K., Bolotnikova, A., Anbarjafari, G.: Edge information based object classification for NAO robots. Cogent Eng. 3(1), 1262571 (2016)CrossRefGoogle Scholar
  16. 16.
    Canny, J.F.: Finding edges and lines in images. Tech. Rep., DTIC Document (1983)Google Scholar
  17. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Fleck, M.M.: Some defects in finite-difference edge finders. IEEE Trans. Pattern Anal. Mach. Intell. 3, 337–345 (1992)CrossRefGoogle Scholar
  19. 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)Google Scholar
  20. 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)Google Scholar
  21. 21.
    Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recognit. 46(3), 1020–1038 (2013)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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)Google Scholar
  25. 25.
    Srinivasan, V., Bhatia, P., Ong, S.H.: Edge detection using a neural network. Pattern Recognit. 27(12), 1653–1662 (1994)CrossRefGoogle Scholar
  26. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 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)CrossRefGoogle Scholar
  28. 28.
    Cumani, A.: Edge detection in multispectral images. CVGIP Graph. Models Image Process. 53(1), 40–51 (1991)CrossRefzbMATHGoogle Scholar
  29. 29.
    Wang, Y., Teoh, E.K.: Object contour extraction using adaptive B-snake model. J. Math. Imaging Vis. 24(3), 295–306 (2006)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Amstutz, S., Fehrenbach, J.: Edge detection using topological gradients: a scale-space approach. J. Math. Imaging Vis. 52(2), 249–266 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 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)Google Scholar
  32. 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)Google Scholar
  33. 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)Google Scholar
  34. 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)Google Scholar
  35. 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)Google Scholar
  36. 36.
    Nalwa, V.S., Binford, T.O.: On detecting edges. IEEE Trans. Pattern Anal. Mach. Intell. 6, 699–714 (1986)CrossRefGoogle Scholar
  37. 37.
    Lee, J.S., Haralick, R.M., Shapiro, L.G.: Morphologic edge detection. IEEE J. Robot. Autom. 3(2), 142–156 (1987)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    Rakesh, R.R., Chaudhuri, P., Murthy, C.: Thresholding in edge detection: a statistical approach. IEEE Trans. Image Process. 13(7), 927–936 (2004)CrossRefGoogle Scholar
  41. 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)Google Scholar
  42. 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)Google Scholar
  43. 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)Google Scholar
  44. 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)Google Scholar
  45. 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)CrossRefGoogle Scholar
  46. 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)Google Scholar
  47. 47.
    Tanchenko, A.: Visual-psnr measure of image quality. J. Vis. Commun. Image Represent. 25(5), 874–878 (2014)CrossRefGoogle Scholar
  48. 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)Google Scholar
  49. 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)CrossRefGoogle Scholar
  50. 50.
    Xie, S., Tu, Z.: Holistically-nested edge detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1395–1403 (2015)Google Scholar
  51. 51.
    Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125, 1–16 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.iCV Lab, Institute of TechnologyUniversity of TartuTartuEstonia
  2. 2.Institute of Computer ScienceUniversity of TartuTartuEstonia
  3. 3.Department of Electrical and Electronic EngineeringHasan Kalyoncu UniversityGaziantepTurkey

Personalised recommendations