Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/65373
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Type: Conference paper
Title: Efficient multi-structure robust fitting with incremental top-k lists comparison
Author: Wong, H.
Chin, T.J.
Yu, J.
Suter, D.
Citation: Computer Vision - ACCV 2010: Proceedings of 10th Asian Conference on Computer Vision, held in Queenstown, New Zealand, Nov 8-12 2010, revised selected papers, part 1 / R. Kimmel, R. Klette and A. Sugimoto (eds.): pp.1-12
Publisher: Springer
Publisher Place: www
Issue Date: 2010
Series/Report no.: Lecture notes on Computer Science ; 6495
ISBN: 9783642192814
ISSN: 0302-9743
1611-3349
Conference Name: Asian Conference on Computer Vision (10th : 2010 : Queenstown, New Zealand)
Statement of
Responsibility: 
Hoi Sim Wong, Tat-Jun Chin, Jin Yu and David Suter
Abstract: Random hypothesis sampling lies at the core of many popular robust fitting techniques such as RANSAC. In this paper, we propose a novel hypothesis sampling scheme based on incremental computation of distances between partial rankings (top-k lists) derived from residual sorting information. Our method simultaneously (1) guides the sampling such that hypotheses corresponding to all true structures can be quickly retrieved and (2) filters the hypotheses such that only a small but very promising subset remain. This permits the usage of simple agglomerative clustering on the surviving hypotheses for accurate model selection. The outcome is a highly efficient multi-structure robust estimation technique. Experiments on synthetic and real data show the superior performance of our approach over previous methods.
Rights: Copyright Springer-Verlag Berlin Heidelberg 2011
RMID: 0020103761
DOI: 10.1007/978-3-642-19282-1_44
Appears in Collections:Computer Science publications

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