Name
Abstract | ||
|---|---|---|
Estimation of local multiple orientations plays an important role in many image processing and computer vision tasks. It has been shown that the detection of orientations in an image patch corresponds to fitting multiple axes to its Fourier transform. In this paper, k-medoids are introduced to detect local multiple orientations in the Fourier domain. Medoids are related to a well-known matrix eigenvector problem. A hierarchical schema with eigensystem and energy distribution analysis is employed to determine the number of orientations in an image patch. The proposed approach detects two types of orientation structure (ridges and edges) without difference. Experimental results on synthetic and real images show that the proposed method can detect multiple orientations with high accuracy and is robust against noise. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5651861 | ICIP |
Keywords | Field | DocType |
fourier transforms,matrix eigenvector problem,image processing,eigensystem,fourier transform,local multiple orientation estimation,local multiple orientations,matrix algebra,image patch,synthetic images,computer vision,energy distribution analysis,eigenvalues and eigenfunctions,real images,fourier domain,k-medoids,estimation,k medoids,robustness,eigenvectors,accuracy,noise | Computer vision,Pattern recognition,Matrix (mathematics),Computer science,Image processing,Robustness (computer science),Fourier transform,Artificial intelligence,Real image,k-medoids,Eigenvalues and eigenvectors,Medoid | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhanghui Kuang | 1 | 56 | 9.91 |
| Guodong Pan | 2 | 1 | 1.38 |
| Kwan-Yee K. Wong | 3 | 108 | 10.26 |