A novel edge detection method based on efficient gaussian binomial filter

(1) * El Houssain Ait Mansour Mail (Department of Risks and Prevention, Office of Geophysics and Mines, France)
(2) Francois Bretaudeau Mail (Department of Risks and Prevention, Office of Geophysics and Mines, France)
*corresponding author

Abstract


Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

Keywords


Image edge detection; Gaussian filter; Image processing; Gaussian binomial filter; Algorithm design and analysis

   

DOI

https://doi.org/10.26555/ijain.v7i2.651
      

Article metrics

Abstract views : 208 | PDF views : 29

   

Cite

   

Full Text

Download

References


[1] T. X. -, “A Novel Image Edge Detection Algorithm based on Prewitt Operator and Wavelet Transform,” Int. J. Adv. Comput. Technol., vol. 4, no. 19, pp. 73–82, Oct. 2012, doi: 10.4156/ijact.vol4.issue19.10.

[2] Yongsheng Gao and M. K. H. Leung, “Face recognition using line edge map,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 6, pp. 764–779, Jun. 2002, doi: 10.1109/TPAMI.2002.1008383.

[3] P. Dollar and C. L. Zitnick, “Structured Forests for Fast Edge Detection,” in 2013 IEEE International Conference on Computer Vision, 2013, pp. 1841–1848, doi: 10.1109/ICCV.2013.231.

[4] J. Wu, Z. Yin, and Y. Xiong, “The Fast Multilevel Fuzzy Edge Detection of Blurry Images,” IEEE Signal Process. Lett., vol. 14, no. 5, pp. 344–347, May 2007, doi: 10.1109/LSP.2006.888087.

[5] Xin Wang, “Laplacian Operator-Based Edge Detectors,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 5, pp. 886–890, May 2007, doi: 10.1109/TPAMI.2007.1027.

[6] A. Piórkowski, “A Statistical Dominance Algorithm for Edge Detection and Segmentation of Medical Images,” 2016, pp. 3–14. doi: 10.1007/978-3-319-39796-2_1

[7] Ş. Öztürk and B. Akdemir, “Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production,” Procedia - Soc. Behav. Sci., vol. 195, pp. 2675–2682, Jul. 2015, doi: 10.1016/j.sbspro.2015.06.477.

[8] A. Aslam, E. Khan, and M. M. S. Beg, “Improved Edge Detection Algorithm for Brain Tumor Segmentation,” Procedia Comput. Sci., vol. 58, pp. 430–437, 2015, doi: 10.1016/j.procs.2015.08.057.

[9] X. Song, X. Zhao, L. Fang, H. Hu, and Y. Yu, “EdgeStereo: An Effective Multi-task Learning Network for Stereo Matching and Edge Detection,” Int. J. Comput. Vis., vol. 128, no. 4, pp. 910–930, Apr. 2020, doi: 10.1007/s11263-019-01287-w.

[10] V. Kothapalli, S. Arora, and M. Hanmandlu, “Edge detection using fractional derivatives and information sets,” J. Electron. Imaging, vol. 27, no. 05, p. 1, Jun. 2018, doi: 10.1117/1.JEI.27.5.051226.

[11] T. Zhu et al., “Generalized Spatial Differentiation from the Spin Hall Effect of Light and Its Application in Image Processing of Edge Detection,” Phys. Rev. Appl., vol. 11, no. 3, p. 034043, Mar. 2019, doi: 10.1103/PhysRevApplied.11.034043.

[12] D. Panagiotidis, A. Abdollahnejad, P. Surový, and K. Kuželka, “Detection of fallen logs from high-resolution UAV images,” New Zeal. J. For. Sci., vol. 49, Mar. 2019, doi: 10.33494/nzjfs492019x26x.

[13] R. Bausys, G. Kazakeviciute-Januskeviciene, F. Cavallaro, and A. Usovaite, “Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method,” Sustainability, vol. 12, no. 2, p. 548, Jan. 2020, doi: 10.3390/su12020548.

[14] A. Ighoyota Ben, O. Nicholas.O., and O. Charles O., “Optimum Fuzzy based Image Edge Detection Algorithm,” Int. J. Image, Graph. Signal Process., vol. 9, no. 4, pp. 44–55, Apr. 2017, doi: 10.5815/ijigsp.2017.04.06.

[15] E. Dong, K. Li, and J. Tong, “FPGA Based Design and Implementation of Improved Edge Detection Algorithm using LOG Operator,” in 2018 IEEE International Conference on Mechatronics and Automation (ICMA), 2018, pp. 2092–2096, doi: 10.1109/ICMA.2018.8484676.

[16] A. Karnam, D. R. Kulkarni, K. P. Sunagar, N. G. Revankar, and M. M. Dixit, “Analysis of Various Edge Detection Techniques,” Bonfring Int. J. Res. Commun. Eng., vol. 6, no. Special Issue, pp. 10–12, Nov. 2016, doi: 10.9756/BIJRCE.8190.

[17] T. S. Gunawan, I. Z. Yaacob, M. Kartiwi, N. Ismail, N. F. Za’bah, and H. Mansor, “Artificial Neural Network Based Fast Edge Detection Algorithm for MRI Medical Images,” Indones. J. Electr. Eng. Comput. Sci., vol. 7, no. 1, p. 123, Jul. 2017, doi: 10.11591/ijeecs.v7.i1.pp123-130.

[18] H. H. Abbass and Z. R. Mousa, “Edge detection of medical images using Markov basis,” Appl. Math. Sci., vol. 11, pp. 1825–1833, 2017, doi: 10.12988/ams.2017.75160.

[19] R. Sigit, E. Triyana, and M. Rochmad, “Cataract Detection Using Single Layer Perceptron Based on Smartphone,” in 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), 2019, pp. 1–6, doi: 10.1109/ICICoS48119.2019.8982445.

[20] W. Jing, T. Jin, and D. Xiang, “SAR image edge detection with recurrent guidance filter,” IEEE Geosci. Remote Sens. Lett., vol. 18, no. 6, pp. 1064–1068, 2020. doi: 10.1109/LGRS.2020.2990688

[21] H. Ye, B. Shen, and S. Yan, “Prewitt edge detection based on BM3D image denoising,” in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2018, pp. 1593–1597. doi: 10.1109/IAEAC.2018.8577643

[22] M. Gholizadeh-Ansari, J. Alirezaie, and P. Babyn, “Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer,” J. Digit. Imaging, vol. 33, no. 2, pp. 504–515, Apr. 2020, doi: 10.1007/s10278-019-00274-4.

[23] F.-Z. Zhao and T. Wang, “Some results for sums of the inverses of binomial coefficients,” Integers, vol. 5, no. 1, p. A22, 2005. Available at: Google Scholar.

[24] E. H. Ait Mansour and S. Barth, “Efficient Approximation of Gaussian Function for Signal and Image Processing Applications,” in 2019 Signal Processing Symposium (SPSympo), 2019, pp. 1–6, doi: 10.1109/SPS.2019.8882020.

[25] S. Eger, “Stirling’s approximation for central extended binomial coefficients,” Am. Math. Mon., vol. 121, no. 4, pp. 344–349, 2014. doi: 10.4169/amer.math.monthly.121.04.344

[26] R. Maini and H. Aggarwal, “Peformance evaluation of various speckle noise reduction filters on medical images,” Int. J. Recent Trends Eng., vol. 2, no. 4, p. 22, 2009. Available at: Google Scholar.

[27] P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, “Contour Detection and Hierarchical Image Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898–916, May 2011, doi: 10.1109/TPAMI.2010.161.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan, and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: ijain@uad.ac.id (paper handling issues)
    info@ijain.org, andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0