Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/60934
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Type: Journal article
Title: A generalized kernel consensus-based robust estimator
Author: Wang, H.
Mirota, D.
Hager, G.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010; 32(1):178-184
Publisher: IEEE Computer Soc
Issue Date: 2010
ISSN: 0162-8828
1939-3539
Statement of
Responsibility: 
Hanzi Wang, Daniel Mirota, Gregory D. Hager
Abstract: In this paper, we present a new adaptive-scale kernel consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as random sample consensus (RANSAC), adaptive scale sample consensus (ASSC), and maximum kernel density estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.
Keywords: Robust statistics; kernel density estimation; model fitting; motion estimation; pose estimation
Rights: © 2010 IEEE
RMID: 0020093485
DOI: 10.1109/TPAMI.2009.148
Appears in Collections:Computer Science publications

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