A generalized Kernel Consensus-based robust estimator.
A generalized Kernel Consensus-based robust estimator.
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DOI：
10.1109/tpami.2009.148
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发表时间：
2010-01
影响因子：
23.6
通讯作者：
Hager GD
中科院分区：
文献类型：
--
作者：
Wang H;Mirota D;Hager GD
在本文中，我们提出了一种新的自适应尺度核一致性(ASKC)稳健估计，它是随机样本一致性(RANSAC)、自适应尺度样本一致性(ASSC)和最大核密度估计(MKDE)等常用稳健估计的推广。ASKC框架基于并统一了基于非参数核密度估计理论的稳健估计器。特别是，我们证明了这些方法中的每一种都是使用特定内核的ASKC的特例。与这些方法一样，ASKC可以容忍50%以上的异常值，但它也可以自动估计内异值的规模。我们将ASKC应用于计算机视觉中的两个重要领域，稳健的运动估计和姿态估计，并给出了在合成数据和真实数据上的比较结果。
In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum Kernel Density Estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.