Volume II-5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 167-172, 2014
https://doi.org/10.5194/isprsannals-II-5-167-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-5, 167-172, 2014
https://doi.org/10.5194/isprsannals-II-5-167-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  28 May 2014

28 May 2014

Automatic Label Completion for Test Field Calibration

J. Hieronymus J. Hieronymus
  • Humboldt-Universität zu Berlin, Department of Computer Science, Computer Vision, Unter den Linden 6, 10099 Berlin, Germany

Keywords: Targets Identification Automation, Geometric Camera Calibration, Distortion, Image Processing

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.