Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11760-018-1283-z | Signal, Image and Video Processing |
Keywords | Field | DocType |
Edge detection, Singular value decomposition, Edge kernel, Thresholding, Segmentation | Noise reduction,Kernel (linear algebra),Singular value decomposition,Singular value,Pattern recognition,Edge detection,Matrix (mathematics),Binary image,Algorithm,Artificial intelligence,Thresholding,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 7 | 1863-1703 |
Citations | PageRank | References |
2 | 0.37 | 30 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Egils Avots | 1 | 12 | 3.23 |
| Hasan Said Arslan | 2 | 2 | 0.37 |
| Lembit Valgma | 3 | 2 | 0.37 |
| Jelena Gorbova | 4 | 3 | 1.41 |
| Gholamreza Anbarjafari | 5 | 347 | 36.51 |