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

journal contribution
posted on 10.06.2019, 11:12 by Egils Avots, Hasan Said Arslan, L. Valgma, Jelena Gorbova, Gholamreza Anbarjafari
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.

Funding

This work has been partially supported by Estonian Information Technology Foundation, Skype Technologies and Estonian Research Council Grant (PUT638), the Scientific and Technological Research Council of Turkey (TUBITAK) 1001 Project (116E097), and the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund.

History

School

  • Loughborough University London

Published in

Signal, Image and Video Processing

Volume

12

Pages

1301 - 1309

Citation

AVOTS, E. .... et al., 2018. A new kernel development algorithm for edge detection using singular value ratios. Signal, Image and Video Processing, 12(7), pp. 1301 - 1309.

Publisher

© Springer London

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2018

Notes

This paper is in closed access.

ISSN

1863-1703

eISSN

1863-1711

Language

en