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.
Partially supported by the U.S. Army Research Laboratory (ARL) Collaborative Technology Alliance contract DAAD19-01-2-0008.
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Qian, G., Chellappa, R. (2002). Bayesian Self-Calibration of a Moving Camera. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47967-8_19
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DOI: https://doi.org/10.1007/3-540-47967-8_19
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