Outlier elimination for robust ellipse and ellipsoid fitting
Outlier elimination for robust ellipse and ellipsoid fitting
复制标题
DOI：
10.1109/camsap.2009.5413262
复制
发表时间：
2009-10
期刊：
影响因子：
--
通讯作者：
Jieqi Yu;Haipeng Zheng;S. Kulkarni;H. Poor
中科院分区：
文献类型：
--
作者：
Jieqi Yu;Haipeng Zheng;S. Kulkarni;H. Poor
提出了一种椭圆/椭球拟合的野值剔除算法。这种两阶段算法采用基于邻近度的离群值检测算法（使用图拉普拉斯算子），然后是类似于随机样本一致性（RANSAC）的基于模型的离群值检测算法。这两个阶段相互补偿，从而可以通过合理的计算来消除各种类型的异常值。仿真结果表明，该算法大大提高了椭圆/椭球拟合的鲁棒性。
In this paper, an outlier elimination algorithm for ellipse/ellipsoid fitting is proposed. This two-stage algorithm employs a proximity-based outlier detection algorithm (using the graph Laplacian), followed by a model-based outlier detection algorithm similar to random sample consensus (RANSAC). These two stages compensate for each other so that outliers of various types can be eliminated with reasonable computation. The outlier elimination algorithm considerably improves the robustness of ellipse/ellipsoid fitting as demonstrated by simulations.