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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/16758
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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.identifier.citationJournal of Mathematical Imaging and Vision, 2005; 23(2):175-183-
dc.identifier.issn0924-9907-
dc.identifier.issn1573-7683-
dc.identifier.urihttp://hdl.handle.net/2440/16758-
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.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-
dc.identifier.doi10.1007/s10851-005-6465-y-
pubs.publication-statusPublished-
dc.identifier.orcidChojnacki, W. [0000-0001-7782-1956]-
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]-
Appears in Collections:Aurora harvest 2
Computer Science publications

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