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dc.contributor.authorQuoc, H.en
dc.contributor.authorChin, T.en
dc.contributor.authorChojnacki, W.en
dc.contributor.authorSuter, D.en
dc.identifier.citationInternational Journal of Computer Vision, 2014; 106(1):93-112en
dc.description.abstractWhen sampling minimal subsets for robust parameter estimation, it is commonly known that obtaining an all-inlier minimal subset is not sufficient; the points therein should also have a large spatial extent. This paper investigates a theoretical basis behind this principle, based on a little known result which expresses the least squares regression as a weighted linear combination of all possible minimal subset estimates. It turns out that the weight of a minimal subset estimate is directly related to the span of the associated points. We then derive an analogous result for total least squares which, unlike ordinary least squares, corrects for errors in both dependent and independent variables. We establish the relevance of our result to computer vision by relating total least squares to geometric estimation techniques. As practical contributions, we elaborate why naive distance-based sampling fails as a strategy to maximise the span of all-inlier minimal subsets produced. In addition we propose a novel method which, unlike previous methods, can consciously target all-inlier minimal subsets with large spans.en
dc.description.statementofresponsibilityQuoc Huy Tran, Tat-Jun Chin, Wojciech Chojnacki, David Suteren
dc.publisherSpringer Verlagen
dc.rights© Springer Science+Business Media New York 2013en
dc.subjectLeast squares; Total least squares; Minimal subsets; Robust fitting; Hypothesis samplingen
dc.titleSampling minimal subsets with large spans for robust estimationen
dc.typeJournal articleen
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]en
dc.identifier.orcidSuter, D. [0000-0001-6306-3023]en
Appears in Collections:Computer Science publications

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