Calibration

"Strong" calibration (or geometric calibration)
    Definition:
      the "strong" calibration of a camera is the computation of the equations of the fonction linking each 3D point in a 3D axis system to a point in a 2D plane in an image axis system. [ATRM89], [Beyer92], [TaGa94].
    The modele used is the pinhole model.
Modèle projection centrale
    The model of the camera is a plane, called image plane, and a center of projection, or optical center. The image m of a 3D point M is the intersection of the image plane with the line passing through M and the optical center P. This line is called a projection ray.

    By using the pinhole model, two sets of parameters can be distinguished:

    • extrinsic parameters
      • position and orientation of the camera in space (that is, position of the optical center and orientation of the image plane in space)
    • intrinsic parameters
      • pixel dimensions, 
      • focal distance, (distance of the optical center to the image plane)
      • position of the principal point on the image (orthogonal projection of the optical center onto the image plane)
    Remark: in the equations giving the coordinates of m from the coordinates of M, the focal distance and the pixel dimensions are linked; mathematically, there are not three parameters, but two, the quotients of the focal distance divided by the pixels dimensions.

    The "strong" calibration can be seen as a parameter estimation problem defined as follows:

    • the model
      • the pinhole model
    • the parameters
      • X0, Y0 and Z0, the camera position in space;
      • theta, phi and psi, the camera orientation in space;
      • f/dx and f/dy the quotients of the focal distance divided by the pixel dimensions (see previous remark);
      • Px and Py, the position of the principal point in the image.
    • the input
      • 3D points from a calibration grid
    • the output
      • the images of these 3D points
    So, a grid is used. Characteristic points are extracted. The solving is performed by a simplex algorithm (Nelder-Mead) with a simulated anealing.

    Dealing with distortions