Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Multibody Grouping from Motion Images
International Journal of Computer Vision
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Point Matching under Large Image Deformations and Illumination Changes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Heteroscedastic Projection Based M-Estimators
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Canonical Stiefel quotient and its application to generic face recognition in illumination spaces
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Journal of Mathematical Imaging and Vision
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
Conjugate gradient on Grassmann manifolds for robust subspace estimation
Image and Vision Computing
Advances in matrix manifolds for computer vision
Image and Vision Computing
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We propose a solution to the problem of robust subspace estimation using the projection based M-estimator. The new method handles more outliers than inliers, does not require a user defined scale of the noise affecting the inliers, handles noncentered data and nonorthogonal subspaces. Other robust methods like RANSAC, use an input for the scale, while methods for subspace segmentation, like GPCA, are not robust. Synthetic data and three real cases of multibody factorization show the superiority of our method, in spite of user independence.