Xiao, X;
Lu, Z;
Xue, J-H;
(2021)
CLUSAC: Clustering Sample Consensus for Fundamental Matrix Estimation.
In:
Proceedings of the IEEE International Conference on Image Processing (ICIP) 2021.
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
In the process of model fitting for fundamental matrix estimation, RANSAC and its variants disregard and fail to reduce the interference of outliers. These methods select correspondences and calculate the model scores from the original dataset. In this work, we propose an inlier filtering method that can filter inliers from the original dataset. Using the filtered inliers can substantially reduce the interference of outliers. Based on the filtered inliers, we propose a new algorithm called CLUSAC, which calculates model quality scores on all filtered inliers. Our approach is evaluated through estimating the fundamental matrix in the dataset kusvod2, and it shows superior performance to other compared RANSAC variants in terms of precision.
| Type: | Proceedings paper |
|---|---|
| Title: | CLUSAC: Clustering Sample Consensus for Fundamental Matrix Estimation |
| Event: | 2021 IEEE International Conference on Image Processing (ICIP) |
| Location: | Anchorage (AK), USA |
| Dates: | 19th-22nd September 2021 |
| ISBN-13: | 978-1-6654-4115-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/icip42928.2021.9506175 |
| Publisher version: | https://doi.org/10.1109/ICIP42928.2021.9506175 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Inlier filter, model quality score, fundamental matrix estimation, RANSAC |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10133570 |
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