CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.01 - January
Issue No.01 - January (2010 vol.32)
Daniel Mirota , The Johns Hopkins University, Baltimore
Gregory D. Hager , The Johns Hopkins University, Baltimore
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.
Robust statistics, model fitting, kernel density estimation, motion estimation, pose estimation.
Daniel Mirota, Gregory D. Hager, "A Generalized Kernel Consensus-Based Robust Estimator", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 1, pp. 178-184, January 2010, doi:10.1109/TPAMI.2009.148