Abstract
Keypoint detection and description in a continuous scale space achieves better robustness to scale change than those in a discretized scale space. State-of-the-art methods first decompose a continuous scale space into M + 1 component images weighted by M-order polynomials of scale σ, and then reconstruct the value at an arbitrary point in the scale space by a linear combination of the component images. However, these methods create the mismatch that, if σ is large, common filter kernels such as Gaussian converge to zero; but the polynomials of σ diverge. This paper presents a more efficient approximation that suppresses this mismatch by normalizing the weighting functions. Experiments show that the proposed method achieves higher performance tradeoff than state-of-the-art methods: it reduces the number of component images and total running time by 20-40% without a sacrifice of stability in keypoints detection.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings |
Publisher | IEEE Computer Society |
Pages | 31-35 |
Number of pages | 5 |
Volume | 2016-August |
ISBN (Electronic) | 9781467399616 |
DOIs | |
Publication status | Published - 2016 Aug 3 |
Event | 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: 2016 Sep 25 → 2016 Sep 28 |
Other
Other | 23rd IEEE International Conference on Image Processing, ICIP 2016 |
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Country | United States |
City | Phoenix |
Period | 16/9/25 → 16/9/28 |
Keywords
- Feature description
- Keypoint detection
- Scale space
- SIFT
- Spectral SIFT
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing