A novel robust method for large numbers of gross errors

In computer vision tasks, it frequently happens that gross noise occupies the absolute majority of the data. Most robust estimators can tolerate no more than 50% gross errors. In this article, we propose a highly robust estimator, called MDPE (maximum density power estimator), employing density estimation and density gradient estimation techniques in the residual space. This estimator can tolerate more than 85% outliers. Experiments illustrate that the MDPE has a higher breakdown point and less errors than other recently proposed similar estimators: least median of squares (LMedS), residual consensus (RESC), and adaptive least kth order squares (ALKS).