Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/60934
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dc.contributor.author | Wang, H. | - |
dc.contributor.author | Mirota, D. | - |
dc.contributor.author | Hager, G. | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010; 32(1):178-184 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.issn | 1939-3539 | - |
dc.identifier.uri | http://hdl.handle.net/2440/60934 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Hanzi Wang, Daniel Mirota, Gregory D. Hager | - |
dc.language.iso | en | - |
dc.publisher | IEEE Computer Soc | - |
dc.rights | © 2010 IEEE | - |
dc.source.uri | http://dx.doi.org/10.1109/tpami.2009.148 | - |
dc.subject | Robust statistics | - |
dc.subject | kernel density estimation | - |
dc.subject | model fitting | - |
dc.subject | motion estimation | - |
dc.subject | pose estimation | - |
dc.title | A generalized kernel consensus-based robust estimator | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TPAMI.2009.148 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 5 Computer Science publications |
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