The Principal Point and CCD Cameras

Abstract

The principal point has long been regarded as one of the fundamental parameters in camera calibration. In the age of film based aerial and large format terrestrial cameras, the principal point could be located by a variety of techniques with a certainty of ϩ Ϫ 10 m (Carman and Brown, 1961) and this was considered sufficient. However, aerial cameras were precision, purpose built, expensive pieces of equipment where the assembly was painstaking and the location of the principal point measured to a known tolerance. In the digital era, photogrammetrists, and many others, are using cameras which have not been specifically designed or built for photogrammetry. For these cameras there is no requirement for the manufacturers to position the lens in a pre-defined location relative to the image sensing plane or for the lens manufacturer to align the lens elements precisely. In fact, deviations from the centre of the sensor can be a considerable percentage of the extent of the sensor (up to 10 per cent for some zoom lenses (Burner, 1995)). This paper discusses the development of methods of obtaining the location of the principal point, considers the relationship between the principal point and other parameters in the functional model, and shows how the location of this point can be estimated with and without recourse to autocollimation methods.

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

“…There exist various approaches for modeling zoom lenses and the influence of zooming on the intrinsic parameters, as well as for modeling other intricate phenomena related to camera calibration, see e.g., [11,16,53,70,96,100,102,128,150,313,469,531,533,532].…”

Evaluation of Photogrammetric Block Orientation Using Quality Descriptors From Statistically Filtered Tie Points

“…For the above reasons it appears crucial to define new strategies to allow a higher control of the quality indicators for the generation of photogrammetric 3D models, which can be a priori deducted by the precision and the accuracy of the tie points selected to carry out the interior and relative orientation of the block. Because self-calibration can be successfully considered reliable only when some conditions are met, for example a well distributed number of 3D targets and a highly convergent network (Clarke et al, 1998b) or to avoid incorrect calibration using a separate camera calibration at the same focal settings used in the project (El-Hakim et al, 2003) the following approach is proposed. The aim is to use a restricted set of images to obtain the internal orientation parameters; these images must fit the following self-calibration requisites:  images must converge on the same part of the object's area;  the object's area must represent a 3D volume, to have a better estimation of the focal length;  a given part of the object must figure in different positions in all the images, for a better estimation of the distortion parameters.…”

VBS RTK GPS-Assisted Self-Calibration Bundle Adjustment for Aerial Triangulation of Fixed-Wing Uas Images for Updating Topographic Maps

“…The Δx and Δy terms consist of various additional parameters based on different models. This study used Leica Photogrammetry Suite Orientation Management (LPS ORIMA) (Erdas INC., 2008) photogrammetric software to perform VBS RTK GPS-assisted self-calibration bundle adjustment AT, which adapts the Brown physical model (Brown, 1976 Because the main lens distortion results from radial distortion (Clarke et al, 1998), only the radial lens distortion parameters (a1, a2, a3) of the Brown additional parameters are used when LPS ORIMA photogrammetric software is used to perform VBS RTK GPS-assisted self-calibration bundle adjustment AT of UAS images. Therefore, the camera parameters used for self-calibration include the principal distance (c), principal point coordinates (x0, y0) and radial lens distortion (a1, a2, a3).…”

“…However, when performing self-calibration bundle adjustment, a set of initial camera parameters is necessary for easy blunder detection. In addition, the main lens distortion results from radial distortion (Clarke et al, 1998), and only the radial lens distortion parameters (a1, a2, a3) of the Brown model are used to perform VBS RTK GPS-assisted self-calibration bundle adjustment AT of UAS images. Therefore, the camera parameters used for self-calibration include the principal distance (c), principal point coordinates (x0, y0), and radial lens distortion (a1, a2, a3).…”