The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Performance Evaluation of RANSAC Family
Abstract
RANSAC (Random Sample Consensus) has been popular in regression problem with samples contaminated with outliers. It has been a milestone of many researches on robust estimators, but there are a few survey and performance analysis on them. This paper categorizes them on their objectives: being accurate, being fast, and being robust. Performance evaluation performed on line fitting with various data distribution. Planar homography estimation was utilized to present performance in real data.
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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
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633
Least Median of Squares Regression
Rousseeuw, P. J. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477105 · Cited by 3,835
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,396
Matching with PROSAC — Progressive Sample Consensus
Chum, O., & Matas, J. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.221 · Cited by 1,096
Parameter estimation techniques: a tutorial with application to conic fitting
Zhang, Z. (1997) · Image and Vision Computing · doi:10.1016/s0262-8856(96)01112-2 · Cited by 975
The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix
Torr, P. H. S., & Murray, D. W. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007927408552 · Cited by 755
Robust regression methods for computer vision: A review
Meer, P., Mintz, D., Rosenfeld, A., & Kim, D. Y. (1991) · International Journal of Computer Vision · doi:10.1007/bf00127126 · Cited by 638
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Xie, Y., Tian, J., & Zhu, X. X. (2020) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2019.2937630 · Cited by 482
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Metadata
- Authors
- Choi, S., Kim, T., & Yu, W.
- Venue
- Procedings of the British Machine Vision Conference 2009
- Publisher
- British Machine Vision Association
- Published
- 2009
- Pages
- 81.1-81.12
- OpenAlex
- W2024548552
- Cited by
- 414
- References
- 23
- FWCI
- 9.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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