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Abstract
Abstract. For geometric camera calibration using a test field and bundle block adjustment it is crucial to identify markers in images and label them according to a known 3D-model of the test field. The identification and labelling can become challenging, especially when the imaging system incorporates strong unknown distortion. This paper presents an algorithm, that automatically completes the labelling of unlabelled anonymous marker candidates given at least three labelled markers and a labelled 3D-model of the test field. The algorithm can be used for extracting information from images as a pre-processing step for a subsequent bundle block adjustment. It identifies an unlabelled marker candidate by referencing it to two, three or four already labelled neighbours, depending on the geometric relationship between the reference points and the candidate. This is achieved by setting up a local coordinate system, that reflects all projection properties like perspective, focal length and distortion. An unlabelled point is then represented in this local coordinate system. These local coordinates of a point are very similar in the corresponding 3D-model and in the image, which is the key idea of identifying an unlabelled point. In experiments the algorithm proofed to be robust against strong and unknown distortion, as long as the distortion does not change within a small sub-image. Furthermore no preliminary information about the focal length or exterior orientation of the camera with respect to the test field is needed.
Highlights
When a camera is used for metric applications or within a network of cameras, the interior orientation of the camera must be known
The local coordinates p of a point P with respect to the local coordinate system with basis B and origin O constructed from some nearby reference points have the property, that they are mostly independent from any underlying distortion and perspective
The fundamental assumption is, that for labelling an unlabelled point, a local coordinate system being constructed from nearby reference points
Summary
When a camera is used for metric applications or within a network of cameras, the interior orientation of the camera must be known. In (Ahn and Rauh, 1998) a collection of commonly used circular coded targets is presented Another option is to use so called exterior orientation devices, which are basically a set of markers with a known geometric constellation, as mentioned in (Fraser and Edmundson, 2000) and (Fraser, 1997). The presented algorithm is not designed to being able to identify the initial markers on its own That means, it relies on external help, e.g. manual identification of markers, constellation identification (see exterior orientation devices in (Fraser and Edmundson, 2000) or for an overview about astronomical star constellation detection approaches (Spratling and Mortari, 2009)), or sophisticated image processing—possibly in conjunction with coded targets. This technique has already been successfully implemented by the author been implemented, but is not part of this paper
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