Artificial Intelligence - Special volume on computer vision
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Computer Vision and Image Understanding
Determining the Epipolar Geometry and its Uncertainty: A Review
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
An Affine Invariant Interest Point Detector
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
An Efficient Solution to the Five-Point Relative Pose Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
How Hard is 3-View Triangulation Really?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Sequential Monte Carlo for Bayesian Matching of Objects with Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Based Feature Matching Across Unrestricted Transformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling and motion control for 2kπ support mode of aircraft model
International Journal of Computer Applications in Technology
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In this paper we propose a method to improve feature point matching results for structure from motion problems, which includes techniques for promoting the correspondence quality using epipolar geometry, fast decision from relative positions of matches and local geometry constraints. We start with feature points extracted from each image and initial set of matches obtained by cross correlation and relaxation principle, then several strategies exploited to detect outliers. Our method is performed before projective reconstruction phase and only two views of scene are needed. Experiments have been carried out and the results demonstrate the efficiency of proposed strategies.