Globally Convergent Autocalibration

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 HuangFaugeras 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.