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Bayesian Self-Calibration of a Moving Camera

  • Gang Qian
  • Rama Chellappa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2351)

Abstract

In this paper, a Bayesian self-calibration approach is proposed using sequential importance sampling (SIS). Given a set of feature correspondences tracked through an image sequence, the joint posterior distributions of both camera extrinsic and intrinsic parameters as well as the scene structure are approximated by a set of samples and their corresponding weights. The critical motion sequences are explicitly considered in the design of the algorithm. The probability of the existence of the critical motion sequence is inferred from the sample and weight set obtained from the SIS procedure. No initial guess for the calibration parameters is required. The proposed approach has been extensively tested on both synthetic and real image sequences and satisfactory performance has been observed.

Keywords

Posterior Distribution Camera Motion Motion Sequence Intrinsic Parameter Structure From Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gang Qian
    • 1
  • Rama Chellappa
    • 1
  1. 1.Center for Automation Research and Department of Electrical and Computer EngineeringUniversity of MarylandUSA

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