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https://hdl.handle.net/2440/56429
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Type: | Conference paper |
Title: | Robust Fitting by Adaptive-Scale Residual Consensus |
Author: | Wang, H. Suter, D. |
Citation: | Computer vision, ECCV 2004: Proceedings of the 8th European Conference on Computer Vision, Part III. May 11-14, 2004 / Tomáš Pajdla and Jiří Matas(eds.): pp.107-118 |
Publisher: | Springer |
Publisher Place: | Berlin |
Issue Date: | 2004 |
Series/Report no.: | Lecture Notes in Computer Science, Computer Vision - ECCV 2004 ; v. 3023 |
ISBN: | 354021982X |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | European Conference on Computer Vision (8th : 2004 : Prague, Czech Republic) |
Editor: | Pajdla, T. Matas, J. |
Statement of Responsibility: | Hanzi Wang and David Suter |
Abstract: | Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called Adaptive-Scale Residual Consensus (ASRC). ASRC scores a model based on both the residuals of inliers and the corresponding scale estimate determined by those inliers. ASRC is very robust to multiple-structural data containing a high percentage of outliers. Compared with RANSAC, ASRC requires no pre-determined inlier threshold as it can simultaneously estimate the parameters of a model and the scale of inliers belonging to that model. Experiments show that ASRC has better robustness to heavily corrupted data than other robust methods. Our experiments address two important computer vision tasks: range image segmentation and fundamental matrix calculation. However, the range of potential applications is much broader than these. |
DOI: | 10.1007/b97871 |
Published version: | http://www.springerlink.com/content/6lxbwmqgatb4/ |
Appears in Collections: | Aurora harvest Computer Science publications |
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