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dc.contributor.authorWang, H.en
dc.contributor.authorMirota, D.en
dc.contributor.authorHager, G.en
dc.identifier.citationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2010; 32(1):178-184en
dc.description.abstractIn 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.en
dc.description.statementofresponsibilityHanzi Wang, Daniel Mirota, Gregory D. Hageren
dc.publisherIEEE Computer Socen
dc.rights© 2010 IEEEen
dc.subjectRobust statistics; kernel density estimation; model fitting; motion estimation; pose estimationen
dc.titleA generalized kernel consensus-based robust estimatoren
dc.typeJournal articleen
pubs.library.collectionComputer Science publicationsen
Appears in Collections:Computer Science publications

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