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 |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 16/9/25 → 16/9/28 |
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Keywords
- Feature description
- Keypoint detection
- Scale space
- SIFT
- Spectral SIFT
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Efficient keypoint detection and description using filter kernel decomposition in scale space. / Okutani, Ryo; Sugimoto, Kenjiro; Kamata, Seiichiro.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. p. 31-35 7532313.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Efficient keypoint detection and description using filter kernel decomposition in scale space
AU - Okutani, Ryo
AU - Sugimoto, Kenjiro
AU - Kamata, Seiichiro
PY - 2016/8/3
Y1 - 2016/8/3
N2 - 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.
AB - 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.
KW - Feature description
KW - Keypoint detection
KW - Scale space
KW - SIFT
KW - Spectral SIFT
UR - http://www.scopus.com/inward/record.url?scp=85006826723&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006826723&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7532313
DO - 10.1109/ICIP.2016.7532313
M3 - Conference contribution
AN - SCOPUS:85006826723
VL - 2016-August
SP - 31
EP - 35
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
ER -