Authors: YongDian Jian ChuSong Chen
Publish Date: 2010/01/16
Volume: 88, Issue: 3, Pages: 489-501
Abstract
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
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