Estimating vision parameters given data with covariances
Date
2000
Authors
Chojnacki, W.
Brooks, M.
Van Den Hengel, A.
Gawley, D.
Editors
Mirmehdi, M.
Thomas, B.
Thomas, B.
Advisors
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Conference paper
Citation
Proceedings of the 11th British Machine Vision Conference 2000: pp.182-191
Statement of Responsibility
Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley
Conference Name
British Machine Vision Conference (11th : 2000 : Bristol, UK)
Abstract
A new parameter estimation method is presented, applicable to many computer vision problems. It operates under the assumption that the data (typically image point locations) are accompanied by covariance matrices characterising data uncertainty. An MLE-based cost function is first formulated and a new minimisation scheme is then developed. Unlike Sampson’s method or the renormalisation technique of Kanatani, the new scheme has as its theoretical limit the true minimum of the cost function. It also has the advantages of being simply expressed, efficient, and unsurpassed in our comparative testing.
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