Name
Abstract | ||
|---|---|---|
Geometric fitting is present in different fields of science, engineering and astronomy. In particular, ellipse shapes are some of the most commonly employed geometric features in digital image analysis and visual pattern recognition. Most geometric and algebraic methods are sensitive to noise and outlier points and so the results are not usually acceptable. In this paper, a robust geometric multicriteria method based on the mean absolute geometric error and the eccentricity to fit an ellipse to set of points is proposed. It is well known that the least mean absolute error criterion leads to robust estimations.The experimental results on different real and synthetic data have shown that the proposed algorithm is robust to outliers. Moreover, it allows us to identify outliers and remove them. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10851-013-0480-1 | Journal of Mathematical Imaging and Vision |
Keywords | Field | DocType |
Ellipse robust fitting,Least mean absolute error,Geometric criteria,Outliers | Geometric data analysis,Mathematical optimization,Algebraic number,Eccentricity (behavior),Mean absolute error,Outlier,Synthetic data,Digital image analysis,Ellipse,Mathematics | Journal |
Volume | Issue | ISSN |
49 | 2 | 0924-9907 |
Citations | PageRank | References |
0 | 0.34 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Muñoz-Pérez | 1 | 119 | 6.81 |
| O. D. Cózar-Macías | 2 | 0 | 1.35 |
| E. B. Blázquez-Parra | 3 | 1 | 1.36 |
| I. Ladrón de Guevara-López | 4 | 3 | 1.17 |