This paper proposes a motion estimation algorithm that is robust to motion discontinuity and noise. The proposed algorithm is constructed by embedding the least median squares (LMedS) of robust statistics into the maximum a posteriori (MAP) estimator. Difficulties in accurate estimation of the motion field arise from the smoothness constraint and the sensitivity to noise. To cope robustly with ...

Abs t rac t . A robust method is introduced for computing the camera motion (the ego-motion) in a static scene. The method is based on detecting a single planar surface in the scene directly from image intensities, and computing its 2D motion in the image plane. The detected 2D motion of the planar surface is used to register the images, so that the planar surface appears stationary. The result...

An algorithm is presented for a robust real-time motion recovery. The algorithm uses point to line matches and L1 error metric to reduce outliers and aperture effects. The line-to-point match is implemented using weighted hough transform over a normalized correlation matrix. The motion parameters minimize theL1 norm and are computed using linear programming.

In a vision guided robotic manipulator it is necessary to estimate the motion parameters of the moving object before some control scheme can be implemented. The classical solution to the general problem of state estimation from a sequence of noisy measurements is the Kalman lter which aims at minimizing the covariance of the estimation error. In this paper certain concepts from robust control s...

A motion segmentation method, based upon robust least K-th order statistical model fitting (LKS), is proposed. Similar LKS based segmentation algorithms have been proposed for range data segmentation but non have been applied to motion segmentation. Moreover, the algorithm we propose here differs from other contemporary approaches using versions of LKS in a number of important ways. Firstly, th...

This paper presents a new algorithm for the motion segmentation task. The proposed algorithm addresses the important issue of the interconnectivity behveen data segmentation, model selection and noise scale estimation. The algorithm is tested on motion segmentation of multiple objects undergoing different types of motion. The results of applying our algorithm to range data segmentation are also...