Name
Abstract | ||
|---|---|---|
Localized phase conveys much more information about im- age structure than magnitude does. For example, phase is used in image reconstruction, edge detection and analysis of textures. We take advantage of the fact that phase is more important on edges and contours than it is in smooth, edge- free regions. Based on this observation, we detect edges as follows: we first calculate the local spatial-frequency trans- form. We then reconstruct the image, using the magnitude and the quantized phase. The effect of phase quantization on the reconstruction error is negligible in smooth areas, while it is very significant around edges. The reconstruction error provides therefore an excellent map of the edges and a skele- ton of the image in the sense of primal sketch. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.5281/zenodo.41645 | EUSIPCO |
Field | DocType | Citations |
Iterative reconstruction,Computer vision,Feature detection (computer vision),Edge detection,Feature extraction,Fourier transform,Skeletonization,Artificial intelligence,Quantization (physics),Detector,Mathematics | Conference | 5 |
PageRank | References | Authors |
0.45 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolay Skarbnik | 1 | 5 | 0.79 |
| Chen Sagiv | 2 | 162 | 10.74 |
| Yehoshua Y. Zeevi | 3 | 610 | 248.69 |