Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/60102
Type: Conference paper
Title: Estimating vision parameters given data with covariances
Author: Chojnacki, W.
Brooks, M.
Van Den Hengel, A.
Gawley, D.
Citation: Proceedings of the 11th British Machine Vision Conference 2000: pp.182-191
Publisher: ILES Central Press
Publisher Place: Bristol, UK
Issue Date: 2000
ISBN: 1901725138
Conference Name: British Machine Vision Conference (11th : 2000 : Bristol, UK)
Statement of
Responsibility: 
Wojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley
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.
Rights: Copyright status unknown
RMID: 0001001741
Published version: http://www.bmva.org/bmvc/2000/contents.htm
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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