我尝试使用类似于Features2D + Homography to find a known object的方法比较图像,但用自写的findHomography()
函数替换findAffine()
。
考虑到离群值,我使用Ceres Solver获得最佳仿射矩阵。
double translation[] = {0, 0};
double angle = 0;
double scaleFactor = 1;
ceres::Problem problem;
for (size_t i = 0; i < points1.size(); ++i) {
problem.AddResidualBlock(
new ceres::AutoDiffCostFunction<AffineResidual, 1, 2, 1, 1>(
new AffineResidual(Eigen::Vector2d(points1[i].x, points1[i].y),
Eigen::Vector2d(points2[i].x, points2[i].y))),
new ceres::HuberLoss(1.0),
translation,
&angle,
&scaleFactor);
}
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.minimizer_progress_to_stdout = true;
ceres::Solver::Summary summary;
Solve(options, &problem, &summary);
谷神星求解器提供LossFunction:
当然,我可以通过获得的矩阵从第一个图像变换关键点坐标,与第二个图像进行比较并得到偏差。但是ceres求解器已经在工作期间在内部完成了。
我该如何找回它?在文档中找不到它。
最佳答案
我有类似的问题。在研究了Ceres库的资源(尤其是ResidualBlock::Evaluate()方法)之后,我得出了一个结论,即残留块没有明确的“异常”状态。损失函数似乎只影响块的最终成本值(这正是您引用的文档中的短语所描述的-“损失函数减少了具有高残差的残差块的影响”)。因此,答案是您无法从Ceres检索离群值,没有此类功能。
解决方法可能是使用已解决的结果计算数据的残差,然后对它们应用损失函数。 LossFunction::Evaluate()的注释可能会有所帮助:
// For a residual vector with squared 2-norm 'sq_norm', this method
// is required to fill in the value and derivatives of the loss
// function (rho in this example):
//
// out[0] = rho(sq_norm),
// out[1] = rho'(sq_norm),
// out[2] = rho''(sq_norm),
//
// Here the convention is that the contribution of a term to the
// cost function is given by 1/2 rho(s), where
//
// s = ||residuals||^2.
//
// Calling the method with a negative value of 's' is an error and
// the implementations are not required to handle that case.
//
// Most sane choices of rho() satisfy:
//
// rho(0) = 0,
// rho'(0) = 1,
// rho'(s) < 1 in outlier region,
// rho''(s) < 0 in outlier region,
//
// so that they mimic the least squares cost for small residuals.
virtual void Evaluate(double sq_norm, double out[3]) const = 0;
关于c++ - 如何从ceres求解器结果中检索离群值?,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/37379557/