Performance evaluation of iterative geometric fitting algorithms

被引：31

作者：

Kanatani, Kenichi ^{[1
]}

Sugaya, Yasuyuki ^{[2
]}

机构：

[1] Okayama Univ, Dept Comp Sci, Okayama 7008530, Japan

[2] Toyohashi Univ Technol, Dept Comp & Informat Sci, Toyohashi, Aichi 4418580, Japan

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2007年 / 52卷 / 02期

关键词：

geometric fitting; ellipse fitting; fundamental matrix; KCR lower bound; convergence performance;

D O I：

10.1016/j.csda.2007.05.013

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The convergence performance of typical numerical schemes for geometric fitting for computer vision applications is compared. First, the problem and the associated KCR lower bound are stated. Then, three well-known fitting algorithms are described: FNS, HEIV, and renormalization. To these, we add a special variant of Gauss-Newton iterations. For initialization of iterations, random choice, least squares, and Taubin's method are tested. Simulation is conducted for fundamental matrix computation and ellipse fitting, which reveals different characteristics of each method. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：1208 / 1222

页数：15

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