稳健轮廓拟合的中值和众数椭圆参数化
Median and Mode Ellipse Parameterization for Robust Contour Fitting
摘要：
在计算机视觉应用中经常出现需要对封闭轮廓进行参数化的问题。本文介绍了一种新的曲线参数化算法，该算法能够将闭合曲线拟合到一组点，同时对数据中存在的离群值和遮挡具有鲁棒性。这种鲁棒性使得该算法适用于计算机视觉应用，其中特征的误分类可能导致离群值。该算法首先将椭圆拟合到来自源数据的多个五点子集。通过确定椭圆集合的中值周长来参数化闭合曲线。生成的曲线不是椭圆，允许参数化任意闭合轮廓。使用的模态周长，而不是中位数周长也进行了探讨。详细比较了所提出的曲线拟合算法和现有的鲁棒椭圆拟合算法。最后，通过燃烧过程中燃料液滴边界的参数化，证明了该算法在计算机视觉应用中的实用性。所提出的算法的性能和现有算法的性能相比，地面实况分割的燃料液滴图像，这表明改进的性能，区域量化和边缘偏差。
英文摘要：
Problems that require the parameterization of closed contours arise frequently in computer vision applications. This article introduces a new curve parameterization algorithm that is able to fit a closed curve to a set of points while being robust to the presence of outliers and occlusions in the data. This robustness property makes this algorithm applicable to computer vision applications where misclassification of features may lead to outliers. The algorithm starts by fitting ellipses to numerous five point subsets from the source data. The closed curve is parameterized by determining the median perimeter of the set of ellipses. The resulting curve is not an ellipse, allowing arbitrary closed contours to be parameterized. The use of the modal perimeter rather than the median perimeter is also explored. A detailed comparison is made between the proposed curve fitting algorithm and existing robust ellipse fitting algorithms. Finally, the utility of the algorithm for computer vision applications is demonstrated through the parameterization of the boundary of fuel droplets during combustion. The performance of the proposed algorithm and the performance of existing algorithms are compared to a ground truth segmentation of the fuel droplet images, which demonstrates improved performance for both area quantification and edge deviation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
