diego's profile - activity

Hello,

I think I'm having some problems with camera calibration. I'm using the sample calibration program provided with several (20) images taken with an iPhone. I get the camera intrinsic matrix K and the distortion coefficients R. I then load such matrices into another program. This program allows the user to select matching features in 2 different undistorted images from which I can take the Fundamental Matrix F and using K I can get the Essential matrix E = K.t() * F * K.

Afterwards, I test both F and E to check for the epipolar constraint, i.e.: x'Fx=0 or x'Ex= where x and x' are the corresponding the user selected. For every matching point, the test for the fundamental matrix yields values very close to 0, while the one for the essential matrix returns values that are as large as 2694990. This is obviously wrong.

From this I can conclude that I must be doing something wrong. I believe the computation for E is right, so that must leave the calibration. What do I need to do for a good calibration?

Thanks

Hello.

I'm developing a program that allows the user to select feature points from several images, matching them. However, this brings a lot of error in the input since it is hard for the user to precisely select the desired pixel. On the other hand, I assume that the user wants to place the feature in areas of interest (building/window/door corners, among others). Is there anything I can use to "snap" the user selected feature to the "correct" or most interesting point?

Thanks

All I have is the internal parameters. Is the scale of T related in any way with focal length? I mean, the essential matrix is found with the help of the internal camera matrix. I was wondering if T and the focal length could be related. Another question: If I have multiple images taken with the same camera, and find the pose between pairs, would the translation between such pairs be proportional? i.e.: would all the pairwise translations be in the same "unit"?

Hello!

I'm trying to retrieve the pose between two internally calibrated cameras in an unknown scene and all methods that I have found so far (SVD of the essential matrix, vanishing points and others) return the translation up to a scale. However, I need the (exact) estimation of such translation, in order to apply some reconstruction methods (plane sweep, voxel coloring, etc.).

How can I do this? Can anyone help me?

Hi!

I'm not sure about this, since I'm new myself to opencv, but I actually measure one chessboard square and place the value in there.

Hi

I have a camera and it's camera matrix and distortion coefficients (I used the sample program). I took several overlapping pictures from the same scene and now I want to compute their relative position and rotation.

How can I do this?

My idea is to place the first image at the origin coordinates looking along the z axis and then relate the image i+1 with the image i:

for(int i=1; i<images.size(); ++i){
    previous=images[i-1]
    current=images[i]

    // Find and Match feature points with SURF
    // Points in the previous image
    previous_image_points= ...
    // Match points in the second image
    current_image_points=...

    // Find fundamental matrix
    Mat F = findFundamentalMat(previous_image_points, current_image_points, FM_RANSAC, 0.1,0.99)
    // Essential matrix. K => Camera matrix
    Mat R = K.t()*F*K

    // Find the position and rotation between cameras
    SVD svd(E);
    Matx33d W(0,-1,0,
                  1,0,0,
                  0,0,1);
    Matx33d Winv(0,1,0,
                     -1,0,0,
                     0,0,1);

    Mat_<double> R = svd.u * Mat(W) * svd.vt;
    Mat_<double> t = svd.u.col(2);

    // Current image position and rotation matrix
    Matx34d rotPos = Matx34d(R(0,0), R(0,1), R(0,2), t(0),
                         R(1,0), R(1,1), R(1,2), t(1),
                         R(2,0), R(2,1), R(2,2), t(2));

    // Previous image position and rotation matrix
    // In the case of image 0, it is the identity matrix
    Matx34d previous_rot_pos=....

    // Calculate the final position and rotation matrix.
    // I know that these are 3x4 matrices and can't be multiplied,
    // but I convert them to homogeneous before :)
    rotPos = previous_rot_pos*rotPos
}

This code concatenates position and rotation matrices such that, in the end, their positions are all relative to the first one.

Thank you :)