Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/29483
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Type: Conference paper
Title: What value covariance information in estimating vision parameters?
Author: Brooks, M.
Chojnacki, W.
Gawley, D.
Van Den Hengel, A.
Citation: Eighth IEEE International Conference on Computer Vision, 2001: pp. 302-308
Publisher: IEEE COMPUTER SOCIETY
Publisher Place: LOS ALAMITOS, CALIFORNIA, USA
Issue Date: 2001
ISBN: 0769511430
Conference Name: IEEE International Conference on Computer Vision (8th : 2001 : Vancouver, Canada)
Statement of
Responsibility: 
Brooks, M.J. Chojnacki, W. Gawley, D. van den Hengel, A.
Abstract: Many parameter estimation methods used in computer vision are able to utilise covariance information describing the uncertainty of data measurements. This paper considers the value of this information to the estimation process when applied to measured image point locations. Covariance matrices are first described and a procedure is then outlined whereby covariances may be associated with image features located via a measurement process. An empirical study is made of the conditions under which covariance information enables generation of improved parameter estimates. Also explored is the extent to which the noise should be anisotropic and inhomogeneous if improvements are to be obtained over covariance-free methods. Critical in this is the devising of synthetic experiments under which noise conditions can be precisely controlled. Given that covariance information is, in itself, subject to estimation error tests are also undertaken to determine the impact of imprecise covariance information upon the quality of parameter estimates. Finally, an experiment is carried out to assess the value of covariances in estimating the fundamental matrix from real images
Description: ©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
RMID: 0020012254
DOI: 10.1109/ICCV.2001.10029
Appears in Collections:Computer Science publications

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