Authors:
Stephan Rupp
and
Matthias Elter
Affiliation:
Fraunhofer Institute for Integrated Circuits (IIS), Germany
Keyword(s):
Robustness, Camera Calibration, Optimization, Genetic Algorithm, Heuristics.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Computer Vision, Visualization and Computer Graphics
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Motion, Tracking and Stereo Vision
;
Multi-View Geometry
;
Stereo Vision and Structure from Motion
Abstract:
The estimation of camera parameters is a fundamental step for many image guided applications in the industrial and medical field, especially when the extraction of 3d information from 2d intensity images is in the focus of a particular application. Usually, the estimation process is called camera calibration and it is performed by taking images of a special calibration object. From these shots the image coordinates of the projected calibration marks are extracted and the mapping from the 3d world coordinates to the 2d image coordinates is calculated. To attain a well-suited mapping, the calibration images must suffice certain constraints in order to ensure that the underlying mathmatical algorithms are well-posed. Thus, the quality of the estimation severly depends on the choice of the input images. In this paper we propose a generic calibration framework that is robust against ill-posed images as it determines the subset of images yielding the optimal model fit error with respect to
a certain quality measure.
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