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231223s2010 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2009.148
|2 doi
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|a pubmed25n0643.xml
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Wang, Hanzi
|e verfasserin
|4 aut
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| 245 |
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|a A generalized Kernel Consensus-based robust estimator
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|c 2010
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 25.01.2010
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|a Date Revised 20.10.2021
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|a published: Print
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|a Citation Status MEDLINE
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|a 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
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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|a Mirota, Daniel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hager, Gregory D
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 32(2010), 1 vom: 08. Jan., Seite 178-84
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
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|g volume:32
|g year:2010
|g number:1
|g day:08
|g month:01
|g pages:178-84
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|u http://dx.doi.org/10.1109/TPAMI.2009.148
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|d 32
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|h 178-84
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