Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining the Epipolar Geometry and its Uncertainty: A Review
International Journal of Computer Vision
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
A tutorial on spectral clustering
Statistics and Computing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Pose Priors for Simultaneously Solving Alignment and Correspondence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence
International Journal of Computer Vision
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We present a novel approach that, given two sets of unmatched keypoints, simultaneously estimates the in-plane camera motion and keypoint matches without using photometric information. Standard approaches estimate the epipolar geometry based on putative matches, first established with photometric information, then accepted or rejected using the epipolar constraint. Our method discretizes the space of essential matrices at different levels. It searches for the essential matrix and key-point matches which are the most geometrically coherent. We maximize geometric coherence, that we define as the number of points that can be matched based on the epipolar and unicity constraints. We applied this general framework to sets of images acquired by a moving tripod. We present promising results on simulated and real data.