Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A Sequential Factorization Method for Recovering Shape and Motion From Image Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Robot Vision
Evaluation and Selection of Models for Motion Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Radon-Based Structure from Motion without Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Multiple 3D Motion Estimation via Mode Finding on Lie Groups
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning Layered Motion Segmentation of Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Two-View Multibody Structure-and-Motion with Outliers through Model Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-View Multibody Structure from Motion
International Journal of Computer Vision
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion
International Journal of Computer Vision
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Statistical Estimation and Segmentation of Multiple Subspaces
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
A Model-Selection Framework for Multibody Structure-and-Motion of Image Sequences
International Journal of Computer Vision
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Smooth image segmentation by nonparametric bayesian inference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Multi-way clustering using super-symmetric non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Incorporating non-motion cues into 3d motion segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Multi-body segmentation and motion number estimation via over-segmentation detection
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
International Journal of Computer Vision
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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.