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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 previous approaches, our method consider motion segmentation and its model selection regarding to the number of motion models as an indivisible problem. The proposed algorithm can simultaneously infer the number of motion models, estimate the cluster memberships of correspondence points, and identify the outliers of input data. The key idea is to use MDP models to fully exploit the epipolar constraints before making premature decisions about the number motion models. To handle outliers efficiently, we then incorporate RANSAC within the inference process of MDP models and make them take the advantages of each other. 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 still have satisfactory performance in the presence of various levels of noise and outlier.