FNS, CFNS and HEIV: A unifying approach

被引：19

作者：

Chojnacki, W ^{[1
]}

Brooks, MJ ^{[1
]}

van den Hengel, A ^{[1
]}

Gawley, D ^{[1
]}

机构：

[1] Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia

来源：

JOURNAL OF MATHEMATICAL IMAGING AND VISION | 2005年 / 23卷 / 02期

关键词：

statistical methods; maximum likelihood; (un)constrained minimisation; fundamental matrix; epipolar equation; conic fitting;

D O I：

10.1007/s10851-005-6465-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：175 / 183

页数：9

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