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A new kernel development algorithm for edge detection using singular value ratios
journal contributionposted on 2019-06-10, 11:12 authored 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.
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.
- Loughborough University London
Published inSignal, Image and Video Processing
Pages1301 - 1309
CitationAVOTS, 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
- VoR (Version of Record)
Publisher statementThis 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/
NotesThis paper is in closed access.