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Globally convergent autocalibration using interval analysis

机译:使用区间分析进行全局收敛的自动校准

摘要

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.
机译:我们解决了具有未知常数固有参数的移动摄像机的自动校准问题。通常,现有的自动校准技术使用数值优化算法,这些算法无法保证收敛到正确的结果。为了解决这个问题,我们开发了一种方法,其中采用间隔分支定界法进行数值最小化。由于间隔分析的特性,该方法可以数学确定性和任意精度收敛到全局解,并且它需要用户输入的唯一输入信息是一组点对应关系和一个搜索间隔。成本函数基于基本矩阵的Huang-Faugeras约束。已经研究了最近提出的基于伯恩斯坦多项式形式的区间扩展,以加快搜索解的速度。最后,给出了实验结果。

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