Improved Ellipse Fitting Algorithm with Outlier Removal

Outliers can significantly affect the results of ellipse fitting. Aiming at this problem, an improved ellipse fitting algorithm based on truncated least squares method and two methods based on double point removal are proposed. The truncated least squares method starts with random sampling , in each...

Full description

Saved in:
Bibliographic Details
Published inJi suan ji ke xue Vol. 49; no. 4; pp. 188 - 194
Main Authors Guo, Si-yu, Wu, Yan-dong
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.04.2022
Editorial office of Computer Science
Subjects
Accuracy
Algorithms
Data points
ellipse fitting|ellipse detection|least trimmed square|dual point removal|vision-based measurement
Elliptic fitting
Least squares method
Matching
Outliers (statistics)
Parameter sensitivity
Random sampling
Run time (computers)
Online AccessGet full text

Cover

More Information
Summary:Outliers can significantly affect the results of ellipse fitting. Aiming at this problem, an improved ellipse fitting algorithm based on truncated least squares method and two methods based on double point removal are proposed. The truncated least squares method starts with random sampling , in each iteration, the data point with the smallest current fitting residual is selected as the fitted point set in the next iteration, and finally converges to the fitting result of the non-outlier points occupying the main body of the point set; double point removal The rule starts from the complete set of points to be fitted, and removes a pair of data points whose fitting residuals are the maximum positive and negative values ​​each time until the number of remaining points does not exceed a given ratio. On the image set of the actual part, for The proposed three algorithms and the existing comparison algorithms are tested. The results show that when the number of retained ellipse points is small, the two algorithms b
ISSN:1002-137X
DOI:10.11896/jsjkx.210200040