A new kernel development algorithm for edge detection using singular value ratios
- 24 Downloads
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.
Keywords
Edge detection Singular value decomposition Edge kernel Thresholding SegmentationReferences
- 1.Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)CrossRefGoogle Scholar
- 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.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.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.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.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.Pan, X., Ye, Y., Cheng, J., Wang, D., Jiang, B.: Composite derivative and edge detection. SIViP 8(3), 523–531 (2014)CrossRefGoogle 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)CrossRefGoogle Scholar
- 9.Desolneux, A., Moisan, L., Morel, J.-M.: Edge detection by Helmholtz principle. J. Math. Imaging Vis. 14(3), 271–284 (2001)CrossRefMATHGoogle Scholar
- 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.Marr, D., Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B Biol. Sci. 207(1167), 187–217 (1980)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle Scholar
- 16.Canny, J.F.: Finding edges and lines in images. Tech. Rep., DTIC Document (1983)Google Scholar
- 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)MathSciNetCrossRefMATHGoogle Scholar
- 18.Fleck, M.M.: Some defects in finite-difference edge finders. IEEE Trans. Pattern Anal. Mach. Intell. 3, 337–345 (1992)CrossRefGoogle 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)Google Scholar
- 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.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.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.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.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.Srinivasan, V., Bhatia, P., Ong, S.H.: Edge detection using a neural network. Pattern Recognit. 27(12), 1653–1662 (1994)CrossRefGoogle 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)MathSciNetCrossRefMATHGoogle 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)CrossRefGoogle Scholar
- 28.Cumani, A.: Edge detection in multispectral images. CVGIP Graph. Models Image Process. 53(1), 40–51 (1991)CrossRefMATHGoogle Scholar
- 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.Amstutz, S., Fehrenbach, J.: Edge detection using topological gradients: a scale-space approach. J. Math. Imaging Vis. 52(2), 249–266 (2015)MathSciNetCrossRefMATHGoogle 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)Google Scholar
- 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.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.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.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.Nalwa, V.S., Binford, T.O.: On detecting edges. IEEE Trans. Pattern Anal. Mach. Intell. 6, 699–714 (1986)CrossRefGoogle Scholar
- 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.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.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.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.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.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.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.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.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.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.Tanchenko, A.: Visual-psnr measure of image quality. J. Vis. Commun. Image Represent. 25(5), 874–878 (2014)CrossRefGoogle 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)Google Scholar
- 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.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.Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125, 1–16 (2017)MathSciNetCrossRefGoogle Scholar