Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers
Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers
复制标题
同时拟合和分割具有异常值的多结构数据
DOI：
10.1109/tpami.2011.216
复制
发表时间：
2012-06-01
影响因子：
23.6
通讯作者：
Suter, David
中科院分区：
文献类型：
--
作者：
Wang, Hanzi;Chin, Tat-Jun;Suter, David
我们提出了一个强大的拟合框架，称为自适应核尺度加权假设（AKSWH），分割多结构的数据，即使在存在大量的离群值。我们的框架包含一个新的规模估计称为迭代Kth有序规模估计（IKOSE）。IKOSE可以准确地估计严重损坏的多结构数据的内点规模，并且其本身很有趣，因为它可以用于其他鲁棒估计器。除了IKOSE，我们的框架还包括几个基于加权、聚类和融合假设的原始元素。AKSWH可以同时提供模型实例数量以及每个模型实例的参数和规模的准确估计。我们在实际应用中表现出良好的性能，如直线拟合，圆拟合，范围图像分割，单应性估计，和基于两个视图的运动分割，使用合成数据和真实的图像。
We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiple-structure data and is of interest by itself since it can be used in other robust estimators. In addition to IKOSE, our framework includes several original elements based on the weighting, clustering, and fusing of hypotheses. AKSWH can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously. We demonstrate good performance in practical applications such as line fitting, circle fitting, range image segmentation, homography estimation, and two-view-based motion segmentation, using both synthetic data and real images.