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PAMI
2010

A Generalized Kernel Consensus-Based Robust Estimator

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A Generalized Kernel Consensus-Based Robust Estimator
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 RANSAC (RANdom SAmple Consensus), ASSC (Adaptive Scale Sample Consensus) and MKDE (Maximum Kernel Density Estimator). 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.
Hanzi Wang, Daniel Mirota, Gregory D. Hager
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PAMI
Authors Hanzi Wang, Daniel Mirota, Gregory D. Hager
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