Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/55533
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dc.contributor.authorWang, H.en
dc.date.issued2008en
dc.identifier.citationIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2008 : pp.3385-3388en
dc.identifier.urihttp://hdl.handle.net/2440/55533-
dc.description.abstractRobust model fitting plays an important role in many computer vision applications. In this paper, we propose a new robust estimator — Maximum Kernel Density Estimator (MKDE) based on the nonparametric kernel density estimation technique. It can be viewed as an improved version of our previously proposed Quick Maximum Density Power Estimator (QMDPE) [15]. Compared with QMDPE, MKDE does not require running the mean shift algorithm for each candidate fit. Thus, the computational complexity of MKDE is greatly reduced while the accuracy of MKDE is comparable to QMDPE and outperforms that of other popular robust estimators such as LMedS and RANSAC. We evaluate MKDE in robust line fitting and fundamental matrix estimation. Experiments show that MKDE has achieved promising results.en
dc.description.statementofresponsibilityHanzi Wangen
dc.language.isoenen
dc.publisherIEEEen
dc.subjectmachine vision; robustness; modelfitting; kernel density estimation; algorithmsen
dc.titleMaximum Kernel Density Estimator for Robust Fittingen
dc.typeConference paperen
dc.identifier.rmid0020093993en
dc.contributor.conferenceInternational Conference on Acoustics, Speech and Signal Processing (2008 : Las Vegas, USA)en
dc.identifier.doi10.1109/ICASSP.2008.4518377en
dc.publisher.placeOnlineen
dc.identifier.pubid36625-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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