Abstract
The most popular image matching algorithm SIFT, introduced by D. Lowe a decade ago, has proven to be sufficiently scale invariant to be used in numerous applications. In practice, however, scale invariance may be weakened by various sources of error. The density of the sampling of the Gaussian scale-space and the level of blur in the input image are two of these sources. This article presents an empirical analysis of their impact on the extracted keypoints stability. We prove that SIFT is really scale and translation invariant only if the scale-space is significantly oversampled. We also demonstrate that the threshold on the difference of Gaussians value is inefficient for eliminating aliasing perturbations. © 2014 IEEE.
| Original language | English |
|---|---|
| Title of host publication | 2014 IEEE International Conference on Image Processing, ICIP 2014 |
| Publisher | IEEE |
| Pages | 4847-4851 |
| ISBN (Print) | 9781479957514 |
| DOIs | |
| Publication status | Published - 28 Jan 2014 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Research Keywords
- aliasing
- invariance
- sampling
- scale-space
- SIFT