Ellipse Fitting with Hyperaccuracy
Ellipse Fitting with Hyperaccuracy
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DOI：
10.1093/ietisy/e89-d.10.2653
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发表时间：
2006-05
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影响因子：
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通讯作者：
K. Kanatani
中科院分区：
文献类型：
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作者：
K. Kanatani
为了将椭圆拟合到点序列，ML（最大似然）被认为具有最高的精度。在本文中，我们证明了一种优于机器学习的“超准确”方法的存在。这是通过对 ML 进行误差分析并减去高阶偏差项来实现的。由于 ML 几乎达到了理论精度界限（KCR 下限），因此带来的改进非常小。尽管如此，我们的分析具有理论意义，阐明了 ML 和 KCR 下限之间的关系。
For fitting an ellipse to a point sequence, ML (maximum likelihood) has been regarded as having the highest accuracy. In this paper, we demonstrate the existence of a “hyperaccurate” method which outperforms ML. This is made possible by error analysis of ML followed by subtraction of high-order bias terms. Since ML nearly achieves the theoretical accuracy bound (the KCR lower bound), the resulting improvement is very small. Nevertheless, our analysis has theoretical significance, illuminating the relationship between ML and the KCR lower bound.