Geometric invariants and object recognition
International Journal of Computer Vision
Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence - Special volume on computer vision
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Characterizing the uncertainty of the fundamental matrix
Computer Vision and Image Understanding
On the Optimization Criteria Used in Two-View Motion Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Invariant Representations
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
On the determination of Epipoles using cross-ratios
Computer Vision and Image Understanding
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Saliency, Scale and Image Description
International Journal of Computer Vision
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
MUSE: Robust Surface Fitting using Unbiased Scale Estimates
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Using Geometric Constraints for Matching Disparate Stereo Views of 3D Scenes Containing Planes
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Epipolar Geometry Estimation via RANSAC Benefits from the Oriented Epipolar Constraint
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wide-Baseline Stereo Matching with Line Segments
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Robust feature point matching by preserving local geometric consistency
Computer Vision and Image Understanding
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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We present a novel method that evaluates the geometric consistency of putative point matches in weakly calibrated settings, i.e. when the epipolar geometry but not the camera calibration is known, using only the point coordinates as information. The main idea behind our approach is the fact that each point correspondence in our data belongs to one of two classes (inliers/outlier). The classification of each point match relies on the histogram of a quantity representing the difference between cross ratios derived from a construction involving 6-tuples of point matches. Neither constraints nor scenario dependent parameters/thresholds are needed. Even for few candidate point matches the ensemble of 6-tuples containing each of them turns to provide statistically reliable histograms that prove to discriminate between inliers and outliers. In fact, in most cases a random sampling among this population is sufficient. Nevertheless, the accuracy of the method is positively correlated to its sampling density leading to an accuracy versus resulting computational complexity trade-off. Theoretical analysis and experiments are given that show the consistent performance of the proposed classification method when applied in inlier/outlier discrimination. The achieved accuracy is favourably evaluated against established methods that employ geometric only information, i.e. those relying on the Sampson, the algebraic and the symmetric epipolar distances. Finally, we also present an application of our scheme in uncalibrated stereo inside a RANSAC framework and compare it to the same as above methods.