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- W1569262834 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." @default.
- W1569262834 created "2016-06-24" @default.
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- W1569262834 date "2002-01-01" @default.
- W1569262834 modified "2023-09-27" @default.
- W1569262834 title "Bayesian Self-Calibration of a Moving Camera" @default.
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- W1569262834 doi "https://doi.org/10.1007/3-540-47967-8_19" @default.
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