Abstract
This paper presents a new self-calibration algorithm of omnidirectional camera from uncalibrated images. First, one parametric non-linear projection model of omnidirectional camera is estimated with the known rotation and translation parameters. After deriving projection model, we can compute an essential matrix of unknown camera motions, and then determine the camera positions. In addition, we showed that LMS (Least-Median-Squares) is most suitable for inlier sampling in our model than other methods: 8-points algorithm and RANSAC (RANdom Sampling Consensus). In the simulation results, we demonstrated that the proposed algorithm can achieve a precise estimation of the omnidirectional model and extrinsic parameters.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hwang, Y., Hong, H. (2006). Estimation of Omnidirectional Camera Model with One Parametric Projection. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_99
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DOI: https://doi.org/10.1007/978-3-540-37258-5_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
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