Robust regression and outlier detection
Robust regression and outlier detection
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Recursive 3-D Visual Motion Estimation Using Subspace Constraints
International Journal of Computer Vision
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multibody Grouping from Motion Images
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Mixtures of probabilistic principal component analyzers
Neural Computation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
N-Dimensional Tensor Voting and Application to Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Frame Correspondence Estimation Using Subspace Constraints
International Journal of Computer Vision
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Smoothness in Layers: Motion segmentation using nonparametric mixture estimation.
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Probabilistic tangent subspace: a unified view
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Joint manifold distance: a new approach to appearance based clustering
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Signal Processing
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA
International Journal of Computer Vision
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Semi-Nonnegative matrix factorization for motion segmentation with missing data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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Recently, subspace constraints have been widely exploited in many computer vision problems such as multibody grouping. Under linear projection models, feature points associated with multiple bodies reside in multiple subspaces. Most existing factorization-based algorithms can segment objects undergoing independent motions. However, intersections among the correlated motion subspaces will lead most previous factorization-based algorithms to erroneous segmentation. To overcome this limitation, in this paper, we formulate the problem of multibody grouping as inference of multiple subspaces from a high-dimensional data space. A novel and robust algorithm is proposed to capture the configuration of the multiple subspace structure and to find the segmentation of objects by clustering the feature points into these inferred subspaces, no matter whether they are independent or correlated. In the proposed method, an Oriented-Frame (OF), which is a multidimensional coordinate frame, is associated with each data point indicating the point's preferred subspace configuration. Based on the similarity between the subspaces, novel mechanisms of subspace evolution and voting are developed. By filtering the outliers due to their structural incompatibility, the subspace configurations will emerge. Compared with most existing factorization-based algorithms that cannot correctly segment correlated motions, such as motions of articulated objects, the proposed method has a robust performance in both independent and correlated motion segmentation. A number of controlled and real experiments show the effectiveness of the proposed method. However, the current approach does not deal with transparent motions and motion subspaces of different dimensions.