The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces
Abstract
We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces such as KAZE [1] and BFSIFT [6] are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) [3, 4] embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. Our features are called Accelerated-KAZE (A-KAZE) due to the dramatic speed-up introduced by FED schemes embedded in a pyramidal framework.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
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Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Pose estimation for augmented reality: a hands-on survey
Marchand, E., Uchiyama, H., & Spindler, F. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2513408 · Cited by 746
Image matching across wide baselines: From paper to practice
Jin, Y., Mishkin, D., Mishchuk, A., Matas, J., Fua, P., Yi, K. M., & Trulls, E. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01385-0 · Cited by 388
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Dellenbach, P., Deschaud, J.-E., Jacquet, B., & Goulette, F. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811849 · Cited by 280
A survey of methods for 3D histology reconstruction
Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207
Metadata
- Authors
- Alcantarilla, P., Nuevo, J., & Bartoli, A.
- Venue
- Procedings of the British Machine Vision Conference 2013
- Publisher
- British Machine Vision Association
- Published
- 2013
- Pages
- 13.1-13.11
- OpenAlex
- W2048710758
- Cited by
- 1,173
- References
- 4
- FWCI
- 19.84
- Type
- conference paper
- Language
- en
- Open access
- closed
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