Precision estimation of a fitted image line

Author

Guo, Jin ; Xu, Guangyou

Author_Institution

Comput. Vision Lab., Tsinghua Univ., Beijing, China

fYear

1989

fDate

0-0 1989

Firstpage

23

Lastpage

26

Abstract

A simple and systematic treatment of the problem of estimating the precision of a fitted image is discussed. It is taken as an error propagation problem, and the general jacobian linearization approach is used to analyze it. Then, the formulas of the estimated precision of the fitted line are derived for two popular line fitting approaches: one is the least-squares line fitting, and the other is the least eigenvalue line fitting, under two typical noise models. To evaluate the obtained precision formulas, some independent Monte-Carlo simulations are made to judge the accuracy and robustness, and then some comparisons with some existing results from complex approaches are made. Several experimental tests in real systems are also mentioned.<>

Keywords

computer vision; curve fitting; least squares approximations; linearisation techniques; Monte-Carlo simulations; accuracy; error propagation; jacobian linearization; least eigenvalue line fitting; least-squares line fitting; line fitting; precision estimation; robustness; Curve fitting; Least squares methods; Linear approximation; Machine vision;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems Engineering, 1989., IEEE International Conference on

Conference_Location

Fairborn, OH, USA

Type

conf

DOI

10.1109/ICSYSE.1989.48612

Filename

48612