Estimating vision parameters given data with covariances

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dc.contributor.authorChojnacki, W.
dc.contributor.authorBrooks, M.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.authorGawley, D.
dc.contributor.conferenceBritish Machine Vision Conference (11th : 2000 : Bristol, UK)
dc.contributor.editorMirmehdi, M.
dc.contributor.editorThomas, B.
dc.date.issued2000
dc.description.abstractA 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.
dc.description.statementofresponsibilityWojciech Chojnacki, Michael J. Brooks, Anton van den Hengel and Darren Gawley
dc.identifier.citationProceedings of the 11th British Machine Vision Conference 2000: pp.182-191
dc.identifier.isbn1901725138
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/60102
dc.language.isoen
dc.publisherILES Central Press
dc.publisher.placeBristol, UK
dc.rightsCopyright status unknown
dc.source.urihttp://www.bmva.org/bmvc/2000/contents.htm
dc.titleEstimating vision parameters given data with covariances
dc.typeConference paper
pubs.publication-statusPublished

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