FNS, CFNS and HEIV: A unifying approach

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dc.contributor.authorChojnacki, W.
dc.contributor.authorBrooks, M.
dc.contributor.authorVan Den Hengel, A.
dc.contributor.authorGawley, D.
dc.date.issued2005
dc.descriptionThe original publication can be found at www.springerlink.com
dc.description.abstractEstimation of parameters from image tokens is a central problem in computer vision. FNS, CFNS and HEIV are three recently developed methods for solving special but important cases of this problem. The schemes are means for finding unconstrained (FNS, HEIV) and constrained (CFNS) minimisers of cost functions. In earlier work of the authors, FNS, CFNS and a core version of HEIV were applied to a specific cost function. Here we extend the approach to more general cost functions. This allows the FNS, CFNS and HEIV methods to be placed within a common framework.
dc.description.statementofresponsibilityWojciech Chojnacki, Michael J. Brooks, Anton Van Den Hengel and Darren Gawley
dc.identifier.citationJournal of Mathematical Imaging and Vision, 2005; 23(2):175-183
dc.identifier.doi10.1007/s10851-005-6465-y
dc.identifier.issn0924-9907
dc.identifier.issn1573-7683
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]
dc.identifier.orcidBrooks, M. [0000-0001-9612-5884]
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]
dc.identifier.urihttp://hdl.handle.net/2440/16758
dc.language.isoen
dc.publisherKluwer Academic Publ
dc.source.urihttp://www.springerlink.com/content/q1213191kjg81275/
dc.subjectstatistical methods, maximum likelihood, (un)constrained minimisation, fundamental matrix, epipolar equation, conic fitting
dc.titleFNS, CFNS and HEIV: A unifying approach
dc.typeJournal article
pubs.publication-statusPublished

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