Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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
Quasiconvex Optimization for Robust Geometric Reconstruction
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multiple View Geometry and the L_"-norm
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Removing Outliers Using The L\infty Norm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Uncertainty Models in Quasiconvex Optimization for Geometric Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
Multiple-View Geometry Under the {$L_\infty$}-Norm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Estimating the Effective Dimension-Reduction Subspace
The Journal of Machine Learning Research
Robust Optimal Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Sparse Structures in L-Infinity Norm Minimization for Structure and Motion Reconstruction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Outlier Removal by Convex Optimization for L-Infinity Approaches
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Decoding by linear programming
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Highly Robust Error Correction byConvex Programming
IEEE Transactions on Information Theory
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We propose a new approach to the problem of robust estimation for a class of inverse problems arising in multiview geometry. Inspired by recent advances in the statistical theory of recovering sparse vectors, we define our estimator as a Bayesian maximum a posteriori with multivariate Laplace prior on the vector describing the outliers. This leads to an estimator in which the fidelity to the data is measured by the L 驴-norm while the regularization is done by the L 1-norm. The proposed procedure is fairly fast since the outlier removal is done by solving one linear program (LP). An important difference compared to existing algorithms is that for our estimator it is not necessary to specify neither the number nor the proportion of the outliers; only an upper bound on the maximal measurement error for the inliers should be specified. We present theoretical results assessing the accuracy of our procedure, as well as numerical examples illustrating its efficiency on synthetic and real data.