Abstract
This paper presents a framework for random sampling nonlinear optimization for camera self-calibration with modeling of the camera intrinsic parameter space. The focal length is modeled using a Gaussian distribution derived from the results of the Kruppa equations, while the optical center is modeled based on the assumption that the optical center is close to the image center but deviates from it due to some manufacturing imprecision. This model enables us to narrow the search range of parameter space and therefore reduce the computation cost. In addition, a random sampling strategy is utilized in order to avoid local optima, where the samples are drawn according to this model. Experimental results are presented to show the effectiveness of the proposed nonlinear optimization algorithm, even in the under-constrained case involving only two frames.
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Rastgar, H., Dubois, E., Zhang, L. (2010). Random Sampling Nonlinear Optimization for Camera Self-calibration with Modeling of Intrinsic Parameter Space. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_20
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DOI: https://doi.org/10.1007/978-3-642-17277-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17276-2
Online ISBN: 978-3-642-17277-9
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