Name
Abstract | ||
|---|---|---|
This paper addresses the orientation estimation in digital images using gradient-based methods. In particular, the second-moment matrix is used to extract the information about the local orientation and the degree of anisotropy in the image, mainly in structured and sinusoid-like textured images. Keeping in mind that the extent of gradient fields smoothing should be decent to extract as faithfully as possible the local orientation information, an algorithm for the estimation of the Gaussian kernel size used in computing the second-moment matrix is proposed. The results obtained on various textured images highlight the strength of the proposed approach in successfully extracting the local variation of orientations in the underlying image, which paves the way towards the accurate extraction of image structures, used in different applications like image synthesis, for example. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IWSSIP.2016.7502721 | 2016 International Conference on Systems, Signals and Image Processing (IWSSIP) |
Keywords | Field | DocType |
Gaussian Kernel,Image Structure,Local Orientation,Second-Moment Matrix,Textured Image | Kernel (linear algebra),Computer vision,Feature detection (computer vision),Pattern recognition,Computer science,Matrix (mathematics),Digital image,Smoothing,Artificial intelligence,Kernel (image processing),Gaussian function,Second moment of area | Conference |
ISSN | Citations | PageRank |
2157-8672 | 1 | 0.38 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adib Akl | 1 | 25 | 5.33 |
| Joe Iskandar | 2 | 1 | 0.72 |