A theory of self-calibration of a moving camera
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - 1998 Marr Prize
Critical Motions for Auto-Calibration When Some Intrinsic Parameters Can Vary
Journal of Mathematical Imaging and Vision
Camera Self-Calibration: Theory and Experiments
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Critical Motion Sequences for Monocular Self-Calibration and Uncalibrated Euclidean Reconstruction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Continuous stereo self-calibration by camera parameter tracking
IEEE Transactions on Image Processing
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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.