Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/90183
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Type: Journal article
Title: Enforcing consistency constraints in uncalibrated multiple homography estimation using latent variables
Author: Chojnacki, W.
Szpak, Z.
Brooks, M.
van Den Hengel, A.
Citation: Machine Vision and Applications, 2015; 26(2-3):401-422
Publisher: Springer Berlin Heidelberg
Issue Date: 2015
ISSN: 0932-8092
1432-1769
Statement of
Responsibility: 
Wojciech Chojnacki, Zygmunt L. Szpak, Michael J. Brooks, Anton van den Hengel
Abstract: An approach is presented for estimating a set of interdependent homography matrices linked together by latent variables. The approach allows enforcement of all underlying consistency constraints while accounting for the arbitrariness of the scale of each individual matrix. The input data is assumed to be in the form of a set of homography matrices individually estimated from image data with no regard to the consistency constraints, appended by a set of error covariances, each characterising the uncertainty of a corresponding homography matrix. A statistically motivated cost function is introduced for upgrading, via optimisation, the input data to a set of homography matrices satisfying the constraints. The function is invariant to a change of any of the individual scales of the input matrices. The proposed approach is applied to the particular problem of estimating a set of homography matrices induced by multiple planes in the 3D scene between two views. An optimisation algorithm for this problem is developed that operates on natural underlying latent variables, with the use of those variables ensuring that all consistency constraints are satisfied. Experimental results indicate that the algorithm outperforms previous schemes proposed for the same task and is fully comparable in accuracy with the ‘gold standard’ bundle adjustment technique, rendering the whole approach both of practical and theoretical interest. With a view to practical application, it is shown that the proposed algorithm can be incorporated into the familiar random sampling and consensus technique, so that the resulting modified scheme is capable of robust fitting of fully consistent homographies to data with outliers.
Keywords: Multiple homographies; Consistency constraints; Latent variables; Multi-projective parameter estimation; Scale invariance; Maximum likelihood; Covariance
Rights: © Springer-Verlag Berlin Heidelberg 2015
RMID: 0030024763
DOI: 10.1007/s00138-015-0660-7
Appears in Collections:Computer Science publications

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