A generalized Kernel Consensus-based robust estimator

Abstract

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.

Fig. 1

The procedure of the TSSE-like scale estimator. (a) The input two-line data with 70 percent outliers. The detected peak and valley with correct parameters (b) and incorrect parameters (c).

Fig. 2

The ASKC estimation procedure.

Fig. 3

Line fitting and extraction by the robust estimators (result by RANSAC is not shown).

Fig. 4

Plane fitting and extraction by the robust estimators (result by RANSAC is not shown).

Fig. 5

(a), (b) Two examples showing the challenges present in endoscopic sinus images. A snapshot showing the feature matches (c) and the matches selected by ASKC1 (d) on the left undistorted image.