Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2017
Abstract
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.
Discipline
Computer and Systems Architecture
Publication
Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017, Honolulu, USA, July 21-26
First Page
2828
Last Page
2837
ISBN
1063-6919
Identifier
10.1109/CVPR.2017.302
Publisher
IEEE
City or Country
Honolulu, USA
Citation
BIAN, Jiawang; LIN, Wen-yan; YASUYUKI, Matsushita; YEUNG, Sai-Kit; NGUYEN, Tan-Dat; and CHENG, Ming-Ming.
GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence. (2017). Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2017, Honolulu, USA, July 21-26. 2828-2837.
Available at: https://ink.library.smu.edu.sg/sis_research/4805
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CVPR.2017.302