Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/58388
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Type: Conference paper
Title: Multi-projective Parameter Estimation for Sets of Homogeneous Matrices
Author: Chojnacki, W.
Hill, R.
Van Den Hengel, A.
Brooks, M.
Citation: Digital Image Computing: Techniques and Applications (DICTA) Proceedings, 2009: pp. 119-124
Publisher: IEEE
Publisher Place: California
Issue Date: 2009
ISBN: 9780769538662
Conference Name: Digital Image Computing: Techniques and Applications (2009 : Melbourne, Australia)
Statement of
Responsibility: 
Wojciech Chojnacki, Rhys Hill, Anton van den Hengel, Michael J. Brooks
Abstract: A number of problems in computer vision require the estimation of a set of matrices, each of which is defined only up to an individual scale factor and represents the parameters of a separate model, under the assumption that the models are intrinsically interconnected. One example of such a set is a family of fundamental matrices sharing an infinite homography. Here an approach is presented to estimating a general set of interdependent matrices defined to within separate scales. The input data is assumed to consist of individually estimated matrices for particular models, which when considered collectively may fail to satisfy the constraints representing the inter-model relationships. Two cost functions are proposed for upgrading, via optimisation, the data of this sort to a collection of matrices satisfying the inter- model constraints. One of these functions incorporates error covariances. Each function is invariant to any change of scale for the input estimates. The proposed approach is applied to the particular problem of estimating a set of fundamental matrices of the form of the example set above. Experimental results are given which demonstrate the effectiveness of the approach.
Keywords: multi-projective parameter estimation; scale independence; maximum likelihood; covariance; homogeneous matrix; fundamental matrix; homography; infinite homography
Rights: © 2009 IEEE
RMID: 0020095978
DOI: 10.1109/DICTA.2009.27
Published version: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5384537
Appears in Collections:Computer Science publications

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