<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hendrik Schilling</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Marcel Gutsche</style></author><author><style face="normal" font="default" size="100%">Hamza Aziz-Ahmad</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A fractal calibration pattern for improved camera calibration</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.5445/KSP/1000059899</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Camera calibration, crucial for computer vision tasks, often relies on planar calibration targets to calibrate the camera parameters. This work explores a planar, fractal, self-identifying calibration pattern, which provides a high density of calibration points for a large range of magnification factors. An evaluation on ground truth data shows the target provides very high accuracy over a wide range of conditions.</style></abstract></record></records></xml>