Abstract-This paper details a novel approach to specifying the optimal pose of planar targets in camera calibration that both reduces the number of images required, and improves the parameter estimates. This is accomplished within a semisupervised strategy where virtual images of planar calibration targets are generated and displayed. These virtual targets are then replicated by the user to generate an image network with optimal geometry for the recovery of the camera parameters. Optimal planar pose is specified by enforcing maximum independence within the calibration constraints offered by each image within the network. This solution space is further refined to ensure that the generated target pose is suitable for easy acquisition and subsequent feature extraction processes. The results on simulated and real data demonstrate that proper consideration of image network geometry directly leads to more accurate camera parameter estimates.
Abstract:This paper details a novel approach to automatically selecting images which improve camera calibration results. An algorithm is presented which identifies calibration images that inherently improve camera parameter estimates based on their geometric configuration or image network geometry. Analysing images in a more intuitive geometric framework allows image networks to be formed based on the relationship between their world to image homographies. Geometrically, it is equivalent to enforcing maximum independence between calibration images, this ensures accuracy and stability when solving the planar calibration equations. A webcam application using the proposed strategy is presented. This demonstrates that careful consideration of image network geometry, which has largely been neglected within the community, can yield more accurate parameter estimates with less images.
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