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We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general. 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 converges to the global solution with mathematical certainty and arbitrary accuracy and the only input information it requires from the user are a set of point correspondences and a search interval. The cost function is based on the Huang-Faugeras constraint of the essential matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, experimental results are presented.
Image processing and computer vision, camera calibration, modeling from video, interval arithmetic, 3D/stereo scene analysis, self-calibration.
Alessandro Busti, Michela Farenzena, Arrigo Benedetti, Andrea Fusiello, "Globally Convergent Autocalibration Using Interval Analysis", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 1633-1638, December 2004, doi:10.1109/TPAMI.2004.125
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