Maximum kernel density estimator for robust fitting

Robust model fitting plays an important role in many computer vision applications. In this paper, we propose a new robust estimator - maximum kernel density estimator (MKDE) based on the nonparametric kernel density estimation technique. It can be viewed as an improved version of our previously proposed quick maximum density power estimator (QMDPE) (H. Wang and D. Suter, 2004). Compared with QMDPE, MKDE does not require running the mean shift algorithm for each candidate fit. Thus, the computational complexity of MKDE is greatly reduced while the accuracy of MKDE is comparable to QMDPE and outperforms that of other popular robust estimators such as LMedS and RANSAC. We evaluate MKDE in robust line fitting and fundamental matrix estimation. Experiments show that MKDE has achieved promising results.