The use of convolution operators in the tasks of edge detection

Authors

DOI:

https://doi.org/10.15587/1729-4061.2015.56548

Keywords:

the Kenny algorithm, the Sobel operator, the Roberts operator, the Prewitt operator, detection of the edges, gradient

Abstract

Despite the prevalence of the Kenny algorithm in the edge detection, insufficient attention has been paid to the optimal selection of the convolution matrix. The paper describes typical algorithms to detect the object edges in the image and use the peculiarities of convolution operators in the Kenny algorithm. The research uses a base image size of 13,225 units. Thus, the experiments have proved that the Sobel operator is optimal, in general, for the Kenny algorithm. We have also considered the Roberts operator and the Previtt operator as alternatives and proved that they effectively process individual cases but generally give worse results. We have made a comparative analysis of advantages and disadvantages of all the operators. The paper presents an example of a detailed calculation of the gradient by using the Sobel operator in the Kenny algorithm after the preceding use of the Gaussian filter. The result of the study is verification of the  optimal choice of the Sobel operator for the Kenny algorithm.

Author Biographies

Сергій Олександрович Петров, Sumy State University Rimskogo-Korsakova 2, Sumy, Ukraine

Ph.D.

Department of Computer Science

Ігор Олександрович Марченко, Sumy State University Rimskogo-Korsakova 2, Sumy, Ukraine

post-graduate student

Department of Computer Science

Борис Олександрович Дібров, Sumy State University Rimskogo-Korsakova 2, Sumy, Ukraine

post-graduate student

Department of Computer Science

References

  1. Forsajt, D., Pons, Zh. (2004). Komp'juternoe zrenie. Sovremennyj podhod. Moscow: Vil'jams, 928.
  2. Szeliski, R. (2011). Computer Vision: Algorithms and Applications. London: Springer, 812. doi: 10.1007/978-1-84882-935-0
  3. Bilyns'kyj, J. (2010). Metody obrobky zobrazhen' v komp’juteryzovanyh optyko-elektronnyh systemah. Vinnycja: VNTU, 272.
  4. Li, J., Ding, S. (2011). A Researchon Improved Canny Edge Detection Algorithm. Communications in Computer and Information Science, 228, 102–108. doi: 10.1007/978-3-642-23223-7_13
  5. Harris, C., Stephens, M. (1988). A Combined Cornerand Edge Detector. Proceedings of the Alvey Vision Conference. Plessey: Plessey Research Roke Manor, 23.1–23.6. doi: 10.5244/c.2.23
  6. Hines, E. (1983). Application of edge detection techniques to detection of the bright band in radar data. Image and Vision Computing, 1 (4), 221–226. doi: 10.1016/0262-8856(83)90021-5
  7. Canny, J. F. (1986). A computational approach to edge detection. IEEE Trans Pattern Analysis and Machine Intelligence, PAMI-8 (6), 679–698. doi: 10.1109/tpami.1986.4767851
  8. Conroy, L., Yeung, R., Quirk, S., Phan, T., Smith, W. L. (2015). Retrospective evaluation of visually monitored deep inspiration breath hold for breast cancer patients using edge detection. World Congress on Medical Physics and Biomedical Engineering. Toronto, Canada. doi: 10.1007/978-3-319-19387-8_143
  9. Harun, M. N. H., Nabilah, I., Shazilah, A. N. (2015). Comparative study of edge detection algorithm: vessel wall elasticity measurement for deep vein thrombosis diagnosis. ARPN Journal of Engineering and Applied Sciences, 10 (19), 8635–8641. Available at: http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1015_2772.pdf
  10. Ramamurthy, B., Chandran, K. R. (2011). Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm. International Journal of Computer Applications, 17 (6), 32–37. doi: 10.5120/2222-2831
  11. Law, M. W. K., Chung, A. C. S. (2007). Weighted Local Variance-Based Edge Detection and Its Application to Vascular Segmentation in Magnetic Resonance Angiography. IEEE Transactions on Medical Imaging, 26 (9), 1224–1241. doi: 10.1109/tmi.2007.903231
  12. Hemalatha, R., Deepa, E., Sasipriya, R. (2015). Automated Driving Vehicle Using DigitalImage Processing. IJISET - International Journal of Innovative Science, Engineering and Technology, 2 (9), 445–446. Available at: http://ijiset.com/vol2/v2s9/IJISET_V2_I9_55.pdf
  13. Panetta, K., Wharton, E., Agaian, S. S. (2008). Logarithmic Edge Detection with Applications. Journal of Computers, 3 (9), 11–19. doi: 10.4304/jcp.3.9.11-19
  14. Bradski, G., Kaehler, A. (2008). Learning OpenCV. O’ReillyMedia, 580. Available at: http://www.cse.iitk.ac.in/users/vision/dipakmj/papers/OReilly%20Learning%20OpenCV.pdf
  15. Labeled Faces in the Wild. Available at: http://vis-www.cs.umass.edu/lfw/
  16. Steger, C., Ulrich, M., Wiedemann, C. (2008). Machine Vision Algorithms and Applications. Wiley-VCH, 360.
  17. Johnson, S. (2006). On Digital Photography. O’Reilly Media, 320.
  18. Lysak, N. V., Mironova, Ju. V., Marchenko, I. O., Petrov, S. O. (2015). Pidvyshhennja jakosti rozpiznavannja metodom Violy-Dzhonsa v zadachah informacijnoi' bezpeky pidpryjemstva shljahom poperedn'oi' obrobky zobrazhen'. Optyko-elektronni informacijno-energetychni tehnologii', 29 (1), 70–75

Downloads

Published

2015-12-22

How to Cite

Петров, С. О., Марченко, І. О., & Дібров, Б. О. (2015). The use of convolution operators in the tasks of edge detection. Eastern-European Journal of Enterprise Technologies, 6(4(78), 27–31. https://doi.org/10.15587/1729-4061.2015.56548

Issue

Vol. 6 No. 4(78) (2015): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2015 Сергій Олександрович Петров, Ігор Олександрович Марченко, Борис Олександрович Дібров

Creative Commons License

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

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.