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|Title:||FNS, CFNS and HEIV: A unifying approach|
Van Den Hengel, A.
|Citation:||Journal of Mathematical Imaging and Vision, 2005; 23(2):175-183|
|Publisher:||Kluwer Academic Publ|
|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|
|Appears in Collections:||Computer Science publications|
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