What value covariance information in estimating vision parameters?
Date
2001
Authors
Brooks, M.
Chojnacki, W.
Gawley, D.
Van Den Hengel, A.
Editors
Bob Werner,
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Eighth IEEE International Conference on Computer Vision, 2001: pp. 302-308
Statement of Responsibility
Brooks, M.J. Chojnacki, W. Gawley, D. van den Hengel, A.
Conference Name
IEEE International Conference on Computer Vision (8th : 2001 : Vancouver, Canada)
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
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Dissertation Note
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