Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/146262
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dc.titleRobust self-calibration from single image using RANSAC
dc.contributor.authorWu Q.
dc.contributor.authorShao T.-C.
dc.contributor.authorChen T.
dc.date.accessioned2018-08-21T05:06:49Z
dc.date.available2018-08-21T05:06:49Z
dc.date.issued2007
dc.identifier.citationWu Q., Shao T.-C., Chen T. (2007). Robust self-calibration from single image using RANSAC. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4841 LNCS (PART 1) : 230-237. ScholarBank@NUS Repository.
dc.identifier.isbn9783540768579
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/146262
dc.description.abstractIn this paper, a novel approach for the self-calibration of single image is proposed. Unlike most existing methods, we can obtain the intrinsic and extrinsic parameters based on the information of restricted image points from single image. First, we show how the vanishing point, vanishing line and foot-to-head plane homology can be used to obtain the calibration parameters and then we show our approach how to efficiently adopt RANSAC to estimate them. In addition, noise reduction is proposed to handle the measurement uncertainties of input points. Results in synthetic and real scenes are presented to evaluate the performance of the proposed method.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentOFFICE OF THE PROVOST
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume4841 LNCS
dc.description.issuePART 1
dc.description.page230-237
dc.published.statepublished
Appears in Collections:Staff Publications

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