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tm - Technisches Messen

Plattform für Methoden, Systeme und Anwendungen der Messtechnik

[TM - Technical Measurement: A Platform for Methods, Systems, and Applications of Measurement Technology
]

Editor-in-Chief: Puente León, Fernando / Zagar, Bernhard

12 Issues per year


IMPACT FACTOR 2017: 0.476

CiteScore 2017: 0.46

SCImago Journal Rank (SJR) 2017: 0.239
Source Normalized Impact per Paper (SNIP) 2017: 0.566

Online
ISSN
2196-7113
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Volume 84, Issue 7-8

Issues

On the design of a fractal calibration pattern for improved camera calibration

Über den Entwurf eines fraktalen Kalibriermusters für eine verbesserte Kamerakalibrierung

Hendrik Schilling
  • Corresponding author
  • Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Maximilian Diebold
  • Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Marcel Gutsche
  • Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Bernd Jähne
  • Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-05-15 | DOI: https://doi.org/10.1515/teme-2017-0013

Abstract

Camera calibration, crucial for computer vision tasks, often relies on planar calibration targets to calibrate the camera parameters. This work describes the design of 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 that the target provides very high accuracy over a wide range of conditions.

Zusammenfassung

Eine gute Kamerakalibrierung ist für Computer Vision Aufgaben unentbehrlich und setzt oft auf planare Kalibriermuster, um die Kameraparameter zu ermitteln. Diese Arbeit beschreibt die Entwicklung eines planaren, fraktalen selbst-identifizierenden Kalibriermusters, welches eine hohe Dichte von Kalibrierpunkten für einen weiten Bereich von Abbildungsmaßstäben bereitstellt. Eine Evaluierung auf Ground-truth-Daten demonstiert die hohe Genauigkeit, die sich über einen weiten Parameterbereich erzielen lässt.

Keywords: Camera calibration; high accuracy; passive target; image geometry; image measurement; fiducial marker; self-identifying; image localization

Schlagwörter: Kamerakalibrierung; hohe Genauigkeit; passives Target; Bildgeometrie; Bildmessung; Referenzpunkte; selbstidentifizierend; Bildlokalisierung

About the article

Hendrik Schilling

Hendrik Schilling has received his Diploma in Computer Science in 2015 at the University of Stuttgart. He is currently working on his PhD thesis at the Heidelberg Collaboratory for Image Processing (HCI), in collaboration with Sony Europe Limited. His research includes camera calibration and light eld imaging.

Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg, Germany

Maximilian Diebold

Maximilian Diebold is Leader of the Light-Field Imaging Group at the Heidel- berg Collaboratory for Image Processing since April 2016. He studied Electrical Engineering and Information Technology at the University of Karlsruhe (Dipl.- Ing.) and received his PhD in computer science at the combined faculty for the natural science and mathematics at the University of Heidelberg (Dr.rer.nat.) in April 2016. The thesis was collaborated with Sony Europe Limited. His current interest is to decompose image information by taking a holistic point of view about depth, material, reectance, color, polarization and illumination from a large set of optical measurements i.e. light-eld measurements and use this information to generate what he terms tunable ground truth.

Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg, Germany

Marcel Gutsche

Marcel Gutsche has received his Master degree in Physics from Heidelberg University in 2014. He is currently working on his PhD thesis at the Heidelberg Collaboratory for Image Processing (HCI), in the area of object and BRDF reconstruction from light eld data.

Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg, Germany

Bernd Jähne

Bernd Jähne received his Diploma, Doctoral degree and Habilitation degree in Physics from Heidelberg University in 1977, 1980, and 1985, respectively, and a Habilitation degree in Applied Computer Science from the University of Hamburg-Harburg in 1992. From 1988 to 2003 he hold a research professorship at the Scripps Institution of Oceanography, University of California in San Diego. Since 1994 he is professor at the Interdisciplinary Center for Scientic Computing (IWR) and Institute for Environmental Physics of Heidelberg University in 1994 and since 2008 he heads the Heidelberg Collaboratory for Image Processing (HCI). His research interests include small-scale air-sea interaction and image processing.

Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI), Berliner Straße 43, 69120 Heidelberg, Germany


Revised: 2017-03-29

Accepted: 2017-04-12

Received: 2017-01-31

Published Online: 2017-05-15

Published in Print: 2017-08-28


Sony Europe Limited.


Citation Information: tm - Technisches Messen, Volume 84, Issue 7-8, Pages 440–451, ISSN (Online) 2196-7113, ISSN (Print) 0171-8096, DOI: https://doi.org/10.1515/teme-2017-0013.

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