File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Dynamic RANSAC

TitleDynamic RANSAC
Authors
KeywordsFundamental Matrix
Outlier Detection
RANSAC
Robust Statistics
Issue Date2006
PublisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml
Citation
Proceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6066 How to Cite?
AbstractIn this paper, the classical RANSAC approach is considered for robust matching to remove mismatches (outliers) in a list of putative correspondences. We will examine the justification for using the minimal size of sample set in a RANSAC trial and propose that the size of the sample set should be varied dynamically depending on the noise and data set. Using larger sample set will not increase the number of iterations dramatically but it can provide a more reliable solution. A new adjusting factor is added into the original RANSAC sampling equation such that the equation can model the noisy world better. In the proposed method, the noise variances, percentage of outliers and number of iterations are all estimated iteratively. Experimental results show that the estimated parameters are close to the ground truth. The modification can also be applied to any sampling consensus methods extended from RANSAC. © 2006 SPIE-IS&T.
Persistent Identifierhttp://hdl.handle.net/10722/99379
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorSze, WFen_HK
dc.contributor.authorTang, WKen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-09-25T18:27:34Z-
dc.date.available2010-09-25T18:27:34Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings Of Spie - The International Society For Optical Engineering, 2006, v. 6066en_HK
dc.identifier.issn0277-786Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/99379-
dc.description.abstractIn this paper, the classical RANSAC approach is considered for robust matching to remove mismatches (outliers) in a list of putative correspondences. We will examine the justification for using the minimal size of sample set in a RANSAC trial and propose that the size of the sample set should be varied dynamically depending on the noise and data set. Using larger sample set will not increase the number of iterations dramatically but it can provide a more reliable solution. A new adjusting factor is added into the original RANSAC sampling equation such that the equation can model the noisy world better. In the proposed method, the noise variances, percentage of outliers and number of iterations are all estimated iteratively. Experimental results show that the estimated parameters are close to the ground truth. The modification can also be applied to any sampling consensus methods extended from RANSAC. © 2006 SPIE-IS&T.en_HK
dc.languageengen_HK
dc.publisherS P I E - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xmlen_HK
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineeringen_HK
dc.subjectFundamental Matrixen_HK
dc.subjectOutlier Detectionen_HK
dc.subjectRANSACen_HK
dc.subjectRobust Statisticsen_HK
dc.titleDynamic RANSACen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailTang, WK:wktang@hku.hken_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityTang, WK=rp00175en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1117/12.642764en_HK
dc.identifier.scopuseid_2-s2.0-33645671443en_HK
dc.identifier.hkuros117222en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33645671443&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume6066en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridSze, WF=12804326800en_HK
dc.identifier.scopusauthoridTang, WK=36790135500en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats