Globally convergent autocalibration

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Globally convergent autocalibration (2003)

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by A. Benedetti , A. Busti , M. Farenzena , A. Fusiello

Venue:	In Proceedings of the IEEE International Conference on Computer Vision
Citations:	1 - 1 self

BibTeX

@INPROCEEDINGS{Benedetti03globallyconvergent,
    author = {A. Benedetti and A. Busti and M. Farenzena and A. Fusiello},
    title = {Globally convergent autocalibration},
    booktitle = {In Proceedings of the IEEE International Conference on Computer Vision},
    year = {2003}
}

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Abstract

Existing autocalibration techniques use numerical optimization algorithms that are prone to the problem of local minima. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of Interval Analysis this method is guaranteed to converge to the global solution with mathematical certainty and arbitrary accuracy, and the only input information it requires from the user is a set of point correspondences and a search box. The cost function is based on the Huang-Faugeras constraint of the fundamental matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, some experimental results on synthetic images are presented. 1

Keyphrases

convergent autocalibration arbitrary accuracy point correspondence search box fundamental matrix mathematical certainty local minimum synthetic image huang-faugeras constraint input information autocalibration technique numerical optimization interval analysis interval extension cost function interval branch-and-bound method experimental result bernstein polynomial form numerical minimization global solution

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