Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola

被引：475

作者：

Ahn, SJ

Rauh, W

Warnecke, HJ

机构：

[1] Fraunhofer Inst Mfg Engn & Automat, IPA, D-70569 Stuttgart, Germany

[2] Fraunhofer Soc, D-80636 Munich, Germany

来源：

PATTERN RECOGNITION | 2001年 / 34卷 / 12期

关键词：

orthogonal distance fitting; circle fitting; sphere fitting; conic fitting; orthogonal contacting condition; singular value decomposition; nonlinear least squares; Gauss-Newton iteration;

D O I：

10.1016/S0031-3203(00)00152-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

The least-squares fitting minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined with the orthogonal, or shortest, distances from the given points to the geometric feature to be fitted. For the geometric fitting of circle/sphere/ellipse/hyperbola/parabota, simple and robust nonparametric algorithms are proposed. These are based on the coordinate description of the corresponding point on the geometric feature for the given point, where the connecting line of the two points is the shortest path from the given point to the geometric feature to be fitted. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

引用

页码：2283 / 2303

页数：21

共 49 条

[21]

SINGULAR VALUE DECOMPOSITION AND LEAST SQUARES SOLUTIONS [J].