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GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence

Authors :
Wen-Yan Lin
Jia-Wang Bian
Ming-Ming Cheng
Tan-Dat Nguyen
Yasuyuki Matsushita
Sai-Kit Yeung
Source :
CVPR
Publication Year :
2017
Publisher :
IEEE, 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.

Details

Database :
OpenAIRE
Journal :
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Accession number :
edsair.doi...........a2fee87711c64d86af10c81dda6cb51d
Full Text :
https://doi.org/10.1109/cvpr.2017.302