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  • Research Article
  • Open Access

Calibrating Distributed Camera Networks Using Belief Propagation

EURASIP Journal on Advances in Signal Processing20062007:060696

https://doi.org/10.1155/2007/60696

  • Received: 4 January 2006
  • Accepted: 22 June 2006
  • Published:

Abstract

We discuss how to obtain the accurate and globally consistent self-calibration of a distributed camera network, in which camera nodes with no centralized processor may be spread over a wide geographical area. We present a distributed calibration algorithm based on belief propagation, in which each camera node communicates only with its neighbors that image a sufficient number of scene points. The natural geometry of the system and the formulation of the estimation problem give rise to statistical dependencies that can be efficiently leveraged in a probabilistic framework. The camera calibration problem poses several challenges to information fusion, including overdetermined parameterizations and nonaligned coordinate systems. We suggest practical approaches to overcome these difficulties, and demonstrate the accurate and consistent performance of the algorithm using a simulated 30-node camera network with varying levels of noise in the correspondences used for calibration, as well as an experiment with 15 real images.

Keywords

  • Quantum Information
  • Estimation Problem
  • Practical Approach
  • Statistical Dependency
  • Belief Propagation

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Authors’ Affiliations

(1)
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA

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Copyright

© D. Devarajan and R. J. Radke. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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