School of Information Science and Technology, Xiamen University, Fujian, 361005, China.
IEEE Trans Pattern Anal Mach Intell. 2012 Jun;34(6):1177-92. doi: 10.1109/TPAMI.2011.216.
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.
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），即使在存在大量异常值的情况下，也可以对多结构数据进行分割。我们的框架包含一个新的尺度估计器，称为迭代第 K 阶有序尺度估计器（IKOSE）。IKOSE 可以准确地估计严重污染的多结构数据的内点尺度，并且本身就很有趣，因为它可以用于其他稳健估计器。除了 IKOSE，我们的框架还包括基于假设的加权、聚类和融合的几个原始元素。AKSWH 可以同时提供模型实例的数量以及每个模型实例的参数和尺度的准确估计。我们使用合成数据和真实图像在直线拟合、圆拟合、范围图像分割、单应性估计和基于两视图的运动分割等实际应用中展示了良好的性能。
