Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58218
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Type: Conference paper
Title: Robust Fitting of Multiple Structures: The Statistical Learning Approach
Author: Chin, T.J.
Wang, H.
Suter, D.
Citation: Proceedings of The Twelfth IEEE International Conference on Computer Vision, 2009; pp. 413-420
Publisher: IEEE
Publisher Place: USA
Issue Date: 2009
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781424444205
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (12th : 2009 : Kyoto, Japan)
Statement of
Responsibility: 
Tat-Jun Chin, Hanzi Wang and David Suter
Abstract: We propose an unconventional but highly effective approach to robust fitting of multiple structures by using statistical learning concepts. We design a novel Mercer kernel for the robust estimation problem which elicits the potential of two points to have emerged from the same underlying structure. The Mercer kernel permits the application of well-grounded statistical learning methods, among which nonlinear dimensionality reduction, principal component analysis and spectral clustering are applied for robust fitting. Our method can remove gross outliers and in parallel discover the multiple structures present. It functions well under severe outliers (more than 90% of the data) and considerable inlier noise without requiring elaborate manual tuning or unrealistic prior information. Experiments on synthetic and real problems illustrate the superiority of the proposed idea over previous methods.
Rights: ©2009 IEEE
RMID: 0020096072
DOI: 10.1109/ICCV.2009.5459150
Appears in Collections:Computer Science publications

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