Robust regression and outlier detection
Robust regression and outlier detection
Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
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
Trust-region methods
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Fundamental Matrix Estimation via TIP - Transfer of Invariant Parameters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
High Accuracy Fundamental Matrix Computation and Its Performance Evaluation
IEICE - Transactions on Information and Systems
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Accurate image correspondence is crucial for estimating multiple-view geometry In this paper, we present a registration-based method for improving accuracy of the image correspondences We apply the method to fundamental matrix estimation under practical situations where there are both erroneous matches (outliers) and small feature location errors Our registration-based method can correct feature locational error to less than 0.1 pixel, remedying localization inaccuracy due to feature detectors Moreover, we carefully examine feature similarity based on their post-alignment appearance, providing a more reasonable prior for subsequent outlier detection Experiments show that we can improve feature localization accuracy of the MSER feature detector, which recovers the most accurate feature localization as reported in a recent study by Haja and others As a result of applying our method, we recover the fundamental matrix with better accuracy and more efficiency.