Name
Abstract | ||
|---|---|---|
Orthogonal Fourier-Mellin (OFM) moments have better feature representation capabilities, and are more robust to image noise than the conventional Zernike moments and pseudo-Zernike moments. However, OFM moments have not been extensively used as feature descriptors since they do not possess scale invariance. This paper discusses the drawbacks of the existing methods of extracting OFM moments, and proposes an improved OFM moments. A part of the theory, which proves the improved OFM moments possesses invariance of rotation and scale, is given. The performance of the improved OFM moments is experimentally examined using trademark images, and the invariance of the improved OFM moments is shown to have been greatly improved over the current methods. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1142/S0218001403002757 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Orthogonal Fourier-Mellin moments, invariance, shape, rotation, scale, image retrieval, trademark | Applied mathematics,Fourier analysis,Scale invariance,Image retrieval,Zernike polynomials,Fourier transform,Artificial intelligence,Geometry,Velocity Moments,Invariant (physics),Pattern recognition,Image noise,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 6 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Bin | 1 | 6 | 0.87 |
| Peng Jiaxiong | 2 | 45 | 8.03 |
| Qiu-shi Ren | 3 | 0 | 0.34 |
| Wan-rong Li | 4 | 0 | 0.34 |