International Journal of Computer Vision

, Volume 88, Issue 3, pp 489–501

Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet Process Mixture Models


DOI: 10.1007/s11263-010-0317-y

Cite this article as:
Jian, YD. & Chen, CS. Int J Comput Vis (2010) 88: 489. doi:10.1007/s11263-010-0317-y


We propose a novel motion segmentation algorithm based on mixture of Dirichlet process (MDP) models. In contrast to previous approaches, we consider motion segmentation and its model selection regarding to the number of motion models as an inseparable problem. Our algorithm can simultaneously infer the number of motion models, estimate the cluster memberships of correspondences, and identify the outliers. The main idea is to use MDP models to fully exploit the geometric consistencies before making premature decisions about the number of motion models. To handle outliers, we incorporate RANSAC into the inference process of MDP models. In the experiments, we compare the proposed algorithm with naive RANSAC, GPCA and Schindler’s method on both synthetic data and real image data. The experimental results show that we can handle more motions and have satisfactory performance in the presence of various levels of noise and outlier.


Motion segmentationMixture of Dirichlet process

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Institute of Information ScienceAcademia SinicaTaipeiTaiwan