  Camera calibration is a process of paramount importance in computer vision
applications that require accurate quantitative measurements. The popular
method developed by Zhang relies on the use of a large number of images of a
planar grid of fiducial points captured in multiple poses. Although flexible
and easy to implement, Zhang's method has some limitations. The simultaneous
optimization of the entire parameter set, including the coefficients of a
predefined distortion model, may result in poor distortion correction at the
image boundaries or in miscalculation of the intrinsic parameters, even with a
reasonably small reprojection error. Indeed, applications involving image
stitching (e.g. multi-camera systems) require accurate mapping of distortion up
to the outermost regions of the image. Moreover, intrinsic parameters affect
the accuracy of camera pose estimation, which is fundamental for applications
such as vision servoing in robot navigation and automated assembly. This paper
proposes a method for estimating the complete set of calibration parameters
from a single image of a planar speckle pattern covering the entire sensor. The
correspondence between image points and physical points on the calibration
target is obtained using Digital Image Correlation. The effective focal length
and the extrinsic parameters are calculated separately after a prior evaluation
of the principal point. At the end of the procedure, a dense and uniform
model-free distortion map is obtained over the entire image. Synthetic data
with different noise levels were used to test the feasibility of the proposed
method and to compare its metrological performance with Zhang's method.
Real-world tests demonstrate the potential of the developed method to reveal
aspects of the image formation that are hidden by averaging over multiple
images.
