OpenCV  3.4.11-dev
Open Source Computer Vision
Functions
Robust Estimation

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...
 

Detailed Description

Function Documentation

◆ fundamentalFromCorrespondences7PointRobust()

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).

Parameters
x1Input 2xN Array of 2D points in view 1.
x2Input 2xN Array of 2D points in view 2.
max_errormaximum error (in pixels).
FOutput 3x3 fundamental matrix such that \(x_2^T F x_1=0\).
inliersOutput 1xN vector that contains the indexes of the detected inliers.
outliers_probabilityoutliers 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.

◆ fundamentalFromCorrespondences8PointRobust()

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).

Parameters
x1Input 2xN Array of 2D points in view 1.
x2Input 2xN Array of 2D points in view 2.
max_errormaximum error (in pixels).
FOutput 3x3 fundamental matrix such that \(x_2^T F x_1=0\).
inliersOutput 1xN vector that contains the indexes of the detected inliers.
outliers_probabilityoutliers 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.