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|Title:||A generalized kernel consensus-based robust estimator|
|Citation:||IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010; 32(1):178-184|
|Publisher:||IEEE Computer Soc|
|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|
|Appears in Collections:||Computer Science publications|
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