DOI: https://doi.org/10.14710/jtsiskom.2021.14209

Edge detection comparative analysis using Roberts, Sobel, Prewitt, and Canny methods

Edge Detection Analysis using Roberts, Sobel, Prewitt and Canny Methods

1Master Program of Information Technology, Faculty of Industrial Technology, Universitas, Universitas Ahmad Dahlan. Jl. Ringroad Selatan, Kragilan, Tamanan, Kec. Banguntapan, Bantul, Daerah Istimewa Yogyakarta 55191, Indonesia

2Department of Electrical Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan. Jl. Ringroad Selatan, Kragilan, Tamanan, Kec. Banguntapan, Bantul, Daerah Istimewa Yogyakarta 55191, Indonesia

Received: 4 May 2021; Revised: 27 Jul 2021; Accepted: 31 Jan 2022; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 The authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Edge identification in a digital image is overgrowing in line with advances in computer technology for image processing. Edge detection becomes vital in recognizing the object of an image because the edge of the object in the image contains critical information. The information obtained can be either the size or shape of the object in the image, so the edge quality must be good so that the information contained in it is not lost. This study uses edge detection with the Roberts, Sobel, Prewitt, and Canny methods. The assessment method uses visual analysis, PSNR, Histogram, and Contrast. The study shows that the calculation of PSNR on the Roberts method has the highest value, with an average of 44.19 dB. Sobel, Prewitt, and Canny operators have PSNR values above 30 dB to classify it as a good image. The histogram value with the highest value is the Sobel operator, with an average histogram value of 22.06. In contrast, the highest contrast value is the Canny operator has an average contrast value of 5.08. The Roberts and Canny operators have the best image quality.
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Keywords: edge detection comparison; roberts; sobel; prewitt; canny
Funding: Universitas Ahmad Dahlan

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Section: Original Research Articles
Language : EN
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  1. A. Eleyan and M. S. Anwar, “Multiresolution edge detection using particle swarm optimization,” International Journal of Engineering Science and Application, vol. 1, no. 1, pp. 11–17, 2017. doi: 10.1109/CCAA.2017.8229843
  2. S. Saifullah, Sunardi, and A. Yudhana, “Analisis Perbandingan pengolahan citra asli dan hasil croping untuk identifikasi telur,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 2, no. 3, 2018. doi: 10.28932/jutisi.v2i3.512
  3. D. Herawati and A. R. Kardian, “Analisis deteksi tepi pada citra digital berbasis jpg dengan operator Canny menggunakan matrix laboratory,” Jurnal Ilmu Komputasi, vol. 17, no. 3, pp. 191–208, 2018
  4. V. Dohare and M. P. Parsai, “A review of speed performance evaluation of varios edge detection methods of images,” Indian Journal Computer Science Engineering, vol. 8, no. 2, pp. 128–138, 2017
  5. I. M. B. Saputra, A. Romadhony, and Adiwijaya, “Analisis kompresi lossless JPEG dengan penambahan komplemen terkompresi deflate,” Thesis, Universitas Telkom, Indonesia, 2012
  6. K. R. O. Recio and R. G. Mendoza, “Three-step Approach to edge detection of texts,” Philippine Journal of Science, vol. 148, no. 1, pp. 193–211, 2019
  7. S. Reno and R. Edyal, “Analisa perbandingan deteksi tepi citra foto menggunakan algoritma Robert dan Prewitt,” Multinetics, vol. 2, no. 2, p. 11, 2016. doi: 10.32722/vol2.no2.2016.pp11-15
  8. M. R. H. Mohd Adnan, A. Mohd Zain, H. Haron, M. Zulfaezal Che Azemin, and M. Bahari, “Consideration of Canny edge detection for eye redness image processing,” IOP Conference Series: Materials Science and Engineering, vol. 551, no. 1, 2019. doi: 10.1088/1757-899X/551/1/012045
  9. P. Hidayatullah, Pengolahan Citra Digital. Bandung: Informatika Bandung, 2017
  10. I. G. N. Suryantara, “Implementasi deteksi tepi untuk mendeteksi keretakan tulang orang lanjut usia (manula) pada citra rontgen dengan operator Sobel dan Prewitt,” Jurnal Algoritma Logika dan Komputasi, vol. 1, no. 2, pp. 51–60, 2018. doi: 10.30813/j-alu.v1i2.1368
  11. R. E. Wibowo, R. R. Isnanto, and A. A. Zahra, “Perbandingan kinerja operator Sobel dan Laplacian of Gaussian (log) terhadap acuan Canny untuk mendeteksi tepi citra,” Transient, vol. 3, no. 1, pp. 1–6, 2014
  12. A. S. Ahmed, “Comparative study among sobel, prewitt and canny edge detection operators used in image processing,” Journal of Theoretical and Applied Information Technology, vol. 96, no. 19, pp. 6517–6525, 2018
  13. M. Joshi and A. Vyas, “Comparison of Canny edge detector with Sobel and Prewitt edge detector using different image formats,” International Journal of Engineering Research & Technology, no. 1, pp. 133–137, 2020
  14. I. Riadi, A. Yudhana, and W. Y. Sulistyo, “Analisis perbandingan nilai kualitas citra pada metode deteksi tepi,” Rekayasa Sistem dan Teknologi Informasi, vol. 4, no. 2, pp. 345–351, 2020
  15. Rashmi, M. Kumar, and R. Saxena, “Algorithm and technique on various edge detection : a survey,” Signal & Image Processing: An International Journal, vol. 4, no. 3, pp. 65–75, 2013. doi: 10.5121/sipij.2013.4306
  16. S. Subhasini, “Color Image Edge Detection,” International Journal of Innovations in Engineering and Technology., vol. 8, no. 1, pp. 235–247, 2017, doi: 10.21172/ijiet.81.032
  17. C. Nagaraju, “Canny scale edge detection,” International Journal of Engineering Trends and Technology, vol. 10, pp. 3–7, 2017
  18. R. Kaur and P. Choudhary, “A review of image compression techniques,” International Journal of Computer Applications, vol. 142, no. 1, pp. 8–11, 2016. doi: 10.5120/ijca2016909658
  19. C. Chang, Y. Chou, and J. Shen, “Improving Image quality for JPEG compression,” Knowledge-Based Intelligent Information and Engineering Systems, vol. 3683, pp. 442–448, 2005
  20. R. Mehra, “Estimation of the image quality under different distortions,” International Journal Of Engineering And Computer Science, vol. 5, no. 17291, pp. 17291–17296, 2016. doi: 10.18535/ijecs/v5i7.20
  21. S. Rajkumar and G. Malathi, “A comparative analysis on image quality assessment for real time satellite images,” Indian Journal of Science and Technology, vol. 9, no. 34, 2016. doi: 10.17485/ijst/2016/v9i34/96766
  22. A. Thahab, “A novel secure video steganography technique using temporal lifted wavelet transform and human vision properties,” The International Arab Journal of Irformation Technology, vol. 17, no. 2, pp. 147–153, 2020
  23. M. Yunus, “Perbandingan metode-metode edge detection untuk proses segementasi citra digital,” Jurnal Teknologi Informasi, vol. 3, no. 2, pp. 146–160, 2012
  24. M. N. Ramadhan, “Analisis perbandingan kualitas deteksi tepi pada citra berdasarkan nilai PSNR dan histogram,” Thesis, Universitas Amikom Purwokerto, Indonesia, 2020

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