Robust regression and outlier detection
Robust regression and outlier detection
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
What can be seen in three dimensions with an uncalibrated stereo rig
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Key-Frame Selection and an LMedS-Based Approach to Structure and Motion Recovery
IEICE - Transactions on Information and Systems
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Because the estimation of a fundamental matrix is much dependent on the correspondence, it is important to select a proper inlier set that represents variation of the image due to camera motion. Previous studies showed that a more precise fundamental matrix can be obtained if the evenly distributed points are selected. When the inliers are detected, however, no previous methods have taken into account their distribution. This paper presents two novel approaches to estimate the fundamental matrix by considering the inlier distribution. The proposed algorithms divide an entire image into several sub-regions, and then examine the number of the inliers in each sub-region and the area of each region. In our method, the standard deviations are used as quantitative measures to select a proper inlier set. The simulation results on synthetic and real images show that our consideration of the inlier distribution can achieve a more precise estimation of the fundamental matrix.