OpenCV
Open Source Computer Vision
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Functions | |
double | cv::sfm::fundamentalFromCorrespondences7PointRobust (InputArray x1, InputArray x2, double max_error, OutputArray F, OutputArray inliers, double outliers_probability=1e-2) |
Estimate robustly the fundamental matrix between two dataset of 2D point (image coords space). More... | |
double | cv::sfm::fundamentalFromCorrespondences8PointRobust (InputArray x1, InputArray x2, double max_error, OutputArray F, OutputArray inliers, double outliers_probability=1e-2) |
Estimate robustly the fundamental matrix between two dataset of 2D point (image coords space). More... | |
double cv::sfm::fundamentalFromCorrespondences7PointRobust | ( | InputArray | x1, |
InputArray | x2, | ||
double | max_error, | ||
OutputArray | F, | ||
OutputArray | inliers, | ||
double | outliers_probability = 1e-2 |
||
) |
#include <opencv2/sfm/robust.hpp>
Estimate robustly the fundamental matrix between two dataset of 2D point (image coords space).
x1 | Input 2xN Array of 2D points in view 1. |
x2 | Input 2xN Array of 2D points in view 2. |
max_error | maximum error (in pixels). |
F | Output 3x3 fundamental matrix such that x_2^T F x_1=0. |
inliers | Output 1xN vector that contains the indexes of the detected inliers. |
outliers_probability | outliers probability (in ]0,1[). The number of iterations is controlled using the following equation: k = \frac{log(1-p)}{log(1.0 - w^n )} where k, w and n are the number of iterations, the inliers ratio and minimun number of selected independent samples. The more this value is high, the less the function selects ramdom samples. |
The fundamental solver relies on the 7 point solution. Returns the best error (in pixels), associated to the solution F.
double cv::sfm::fundamentalFromCorrespondences8PointRobust | ( | InputArray | x1, |
InputArray | x2, | ||
double | max_error, | ||
OutputArray | F, | ||
OutputArray | inliers, | ||
double | outliers_probability = 1e-2 |
||
) |
#include <opencv2/sfm/robust.hpp>
Estimate robustly the fundamental matrix between two dataset of 2D point (image coords space).
x1 | Input 2xN Array of 2D points in view 1. |
x2 | Input 2xN Array of 2D points in view 2. |
max_error | maximum error (in pixels). |
F | Output 3x3 fundamental matrix such that x_2^T F x_1=0. |
inliers | Output 1xN vector that contains the indexes of the detected inliers. |
outliers_probability | outliers probability (in ]0,1[). The number of iterations is controlled using the following equation: k = \frac{log(1-p)}{log(1.0 - w^n )} where k, w and n are the number of iterations, the inliers ratio and minimun number of selected independent samples. The more this value is high, the less the function selects ramdom samples. |
The fundamental solver relies on the 8 point solution. Returns the best error (in pixels), associated to the solution F.