In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagran...
Jean-Philippe Tarel, Pierre Charbonnier, Sio-Song ...
This paper presents a novel motion segmentation algorithm on the basis of mixture of Dirichlet process (MDP) models, a kind of nonparametric Bayesian framework. In contrast to pre...
The iterative closest points (ICP) algorithm is widely used for ego-motion estimation in robotics, but subject to bias in the presence of outliers. We propose a random sample conse...
This paper presents a motion estimation and segmentation algorithm based on multiple parametric model estimation that determines the a priori unknown number of motion models prese...
We address the problem of unsupervised segmentation and grouping in 2D and 3D space, where samples are corrupted by noise, and in the presence of outliers. The problem has attract...