Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/16758
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Type: Journal article
Title: FNS, CFNS and HEIV: A unifying approach
Author: Chojnacki, W.
Brooks, M.
Van Den Hengel, A.
Gawley, D.
Citation: Journal of Mathematical Imaging and Vision, 2005; 23(2):175-183
Publisher: Kluwer Academic Publ
Issue Date: 2005
ISSN: 0924-9907
1573-7683
Statement of
Responsibility: 
Wojciech Chojnacki, Michael J. Brooks, Anton Van Den Hengel and Darren Gawley
Abstract: Estimation 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.
Keywords: statistical methods, maximum likelihood, (un)constrained minimisation, fundamental matrix, epipolar equation, conic fitting
Description: The original publication can be found at www.springerlink.com
RMID: 0020050505
DOI: 10.1007/s10851-005-6465-y
Published version: http://www.springerlink.com/content/q1213191kjg81275/
Appears in Collections:Computer Science publications

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