Robust regression and outlier detection
Robust regression and outlier detection
Artificial Intelligence - Special volume on 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
ROR: Rejection of Outliers by Rotations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guided Sampling and Consensus for Motion Estimation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Image matching by multiscale oriented corner correlation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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Many image analysis tasks require an outlier detection procedure to identify the false matches. In this paper, a fast and effective outlier detection method is presented to match images in the uncalibrated case. This method employs a hypothesis test on the consistency of dominant orientations of the feature points to significantly increase the detection speed. Moreover, it can also effectively find the outliers that can not be identified by traditional RANSAC-based methods using epipolar constraint. Note that our method does not require the prior knowledge of camera parameters or the percentage of outliers. The experimental results show that our method outperforms the classical RANSAC-based methods both in speed and in accuracy of the results.