Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Model-based object pose in 25 lines of code
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Artificial Intelligence - Special volume on computer vision
International Journal of Computer Vision
Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Journal of Mathematical Imaging and Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Linear fitting with missing data for structure-from-motion
Computer Vision and Image Understanding
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A Unified Factorization Algorithm for Points, Line Segments and Planes with Uncertainty Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Object Level Grouping for Video Shots
International Journal of Computer Vision
Affine Approximation for Direct Batch Recovery of Euclidian Structure and Motion from Sparse Data
International Journal of Computer Vision
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Weighted and robust learning of subspace representations
Pattern Recognition
Incremental and robust learning of subspace representations
Image and Vision Computing
Estimating 3D shape from degenerate sequences with missing data
Computer Vision and Image Understanding
Robust Factorization Methods Using a Gaussian/Uniform Mixture Model
International Journal of Computer Vision
Endoscopic Feature Tracking and Scale-Invariant Estimation of Soft-Tissue Structures
IEICE - Transactions on Information and Systems
An Iterative Multiresolution Scheme for SFM with Missing Data
Journal of Mathematical Imaging and Vision
An iterative multiresolution scheme for SFM with missing data: Single and multiple object scenes
Image and Vision Computing
Machine Vision and Applications
Improve robustness of sparse PCA by L1-norm maximization
Pattern Recognition
Robust principal component analysis with non-greedy l1-norm maximization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A probabilistic approach to robust matrix factorization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Feature extraction based on Lp-norm generalized principal component analysis
Pattern Recognition Letters
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Factorization algorithms for recovering structure and motion from an image stream have many advantages, but they usually require a set of well-tracked features. Such a set is in generally not available in practical applications. There is thus a need for making factorization algorithms deal effectively with errors in the tracked features. We propose a new and computationally efficient algorithm for applying an arbitrary error function in the factorization scheme. This algorithm enables the use of robust statistical techniques and arbitrary noise models for the individual features. These techniques and models enable the factorization scheme to deal effectively with mismatched features, missing features, and noise on the individual features. The proposed approach further includes a new method for Euclidean reconstruction that significantly improves convergence of the factorization algorithms. The proposed algorithm has been implemented as a modification of the Christy-Horaud factorization scheme, which yields a perspective reconstruction. Based on this implementation, a considerable increase in error tolerance is demonstrated on real and synthetic data. The proposed scheme can, however, be applied to most other factorization algorithms.