Artificial Intelligence - Special volume on computer vision
Rigidity Checking of 3D Point Correspondences Under Perspective Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Calibration of Stationary Cameras
International Journal of Computer Vision
Kruppa's Equations Derived from the Fundamental Matrix
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast and Accurate Algorithms for Projective Multi-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the fundamental matrix by transforming image points in projective space
Computer Vision and Image Understanding
N-Dimensional Tensor Voting and Application to Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Epipole and fundamental matrix estimation using virtual parallax
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An A Contrario Decision Method for Shape Element Recognition
International Journal of Computer Vision
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
International Journal of Computer Vision
On Straight Line Segment Detection
Journal of Mathematical Imaging and Vision
A-contrario Detectability of Spots in Textured Backgrounds
Journal of Mathematical Imaging and Vision
Simultaneous in-plane motion estimation and point matching using geometric cues only
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
How to overcome perceptual aliasing in ASIFT?
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
A probabilistic grouping principle to go from pixels to visual structures
DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
Shape recognition via an a contrario model for size functions
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
An evolutionary infection algorithm for dense stereo correspondence
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Beyond Independence: An Extension of the A Contrario Decision Procedure
International Journal of Computer Vision
Adaptive structure from motion with a contrario model estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Computer Vision and Image Understanding
Accurate Junction Detection and Characterization in Natural Images
International Journal of Computer Vision
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The perspective projections of n physical points on two views (stereovision) are constrained as soon as n ≥ 8. However, to prove in practice the existence of a rigid motion between two images, more than 8 point matches are desirable in order to compensate for the limited accuracy of the matches. In this paper, we propose a computational definition of rigidity and a probabilistic criterion to rate the meaningfulness of a rigid set as a function of both the number of pairs of points (n) and the accuracy of the matches. This criterion yields an objective way to compare, say, precise matches of a few points and approximate matches of a lot of points. It gives a yes/no answer to the question: “could this rigid points correspondence have occurred by chance?”, since it guarantees that the expected number of meaningful rigid sets found by chance in a random distribution of points is as small as desired. It also yields absolute accuracy requirements for rigidity detection in the case of non-matched points, and optimal values of n, depending on the expected accuracy of the matches and on the proportion of outliers. We use it to build an optimized random sampling algorithm that is able to detect a rigid motion and estimate the fundamental matrix when the set of point matches contains up to 90% of outliers, which outperforms the best currently known methods like M-estimators, LMedS, classical RANSAC and Tensor Voting.