AMSAC: An adaptive robust estimator for model fitting
Hanzi Wang, Jinlong Cai, Jianyu Tang · 2013
In this paper, we firstly propose a novel robust scale estimator called AIKOSE. It can estimate the scale of inlier noises by adaptively selecting the optimal value of K in the IKOSE scale estimator. Moreover, based on AIKOSE, we propose a novel robust estimator called AMSAC, which can fit a model without requiring a manually tuned threshold. In the experiments, we demonstrate the performance of AMSAC on line fitting and homography estimation by using both synthetic data and real images. Experimental results show that AM-SAC is more robust than other competing robust estimators.