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| Content Provider | Springer Nature Link |
|---|---|
| Author | Chong, C. W. Raveendran, P. Mukundan, R. |
| Copyright Year | 2003 |
| Abstract | The definition of pseudo-Zernike moments has a form of projection of the image intensity function onto the pseudo-Zernike polynomials, and they are defined using a polar coordinate representation of the image space. Hence, they are commonly used in recognition tasks requiring rotation invariance. However, this coordinate representation does not easily yield a scale invariant function because it is difficult to extract a common scale factor from the radial polynomials. As a result, vision applications generally resort to image normalisation method or using a combination of scale invariants of geometric orradial moments to achieve the corresponding invariants of pseudo-Zernike moments. In this paper, we present a mathematical framework to derive a new set of scale invariants of pseudo-Zernike moments based on pseudo-Zernike polynomials. They are algebraically obtained by eliminating the scale factor contained in the scaled pseudo-Zernike moments. They remain unchanged under equal-shape expansion, contraction and reflection of theoriginal image. They can be directly computed from any scaled image without prior knowledge of the normalisation parameters, or assistance of geometric or radial moments. Their performance is experimentally verified using a set of Chinese and Latin characters. In addition, a comparison of computational speed between the proposed descriptors and the present methods is also presented. |
| Starting Page | 176 |
| Ending Page | 184 |
| Page Count | 9 |
| File Format | |
| ISSN | 14337541 |
| Journal | Pattern Analysis & Applications |
| Volume Number | 6 |
| Issue Number | 3 |
| e-ISSN | 1433755X |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2003-01-01 |
| Publisher Place | London |
| Access Restriction | One Nation One Subscription (ONOS) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Vision and Pattern Recognition |
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