Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/56539
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dc.contributor.authorChen, Peien
dc.contributor.authorSuter, Daviden
dc.date.issued2005en
dc.identifier.urihttp://hdl.handle.net/2440/56539-
dc.descriptionTechnical Report MECSE-32-2005en
dc.description.abstractIt is well known that one can collect the coefficients of the homgraphies between two views into a large, rank deficient matrix. In principle, such an observation implies that one can refine the accuracy of the estimates of the homography coefficients by exploiting the rank constraint. However, the straightforward approach suggested by this observation is impractical because it requires many homographies and it also does not take into account correlations between the errors in the coefficients. In a companion paper [4], we show how to jointly estimate multiple (but a realistic number of) homographies over 2 views. By studying the special structure of the homography, we show that it is possible to calculate the dimension 4 subspace of the homographies from ≥ 3 planes (and, in principle, with even two planes). This contradicts what seems to be the accepted situation regarding the exploitation of the rank-4 constraint amongst homographies: that more than 4 planes are needed to calculate and exploit the dimension 4 subspace. Practical issues arise because the homography coefficients, before rank-constrained refinement, are themselves estimates whose noise covariances need to be characterised and accounted for. In this paper, we develop a statistical analysis allowing for estimation of the covariance matrices required for the calculation of low-rank “denoised” homographiesen
dc.description.statementofresponsibilityPei Chen and David Suteren
dc.language.isoenen
dc.publisherMonash Universityen
dc.titleHomography estimation and heteroscedastic noise - a first order perturbation analysisen
dc.typeReporten
dc.contributor.schoolSchool of Computer Scienceen
Appears in Collections:Computer Science publications

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