School of Computer Science, The University of Adelaide, Adelaide SA 5005, Australia.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):178-84. doi: 10.1109/TPAMI.2009.148.
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.
在本文中，我们提出了一种新的自适应尺度核一致（ASKC）鲁棒估计器，作为流行的和最先进的鲁棒估计器的推广，如随机抽样一致（RANSAC）、自适应尺度样本一致（ASSC）和最大核密度估计器（MKDE）。ASKC 框架基于并使用非参数核密度估计理论统一了这些鲁棒估计器。具体来说，我们表明，这些方法中的每一种都是使用特定核的 ASKC 的特例。与这些方法一样，ASKC 可以容忍超过 50%的异常值，但它也可以自动估计内点的尺度。我们将 ASKC 应用于计算机视觉中的两个重要领域，即鲁棒运动估计和姿态估计，并在合成数据和真实数据上展示了比较结果。
