全局收敛的自动校准

A. Benedetti, Alessandro Busti, M. Farenzena, Andrea Fusiello
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引用次数: 3

摘要

现有的自动校准技术使用数值优化算法,容易出现局部极小值问题。为了解决这个问题,我们开发了一种采用区间分支定界法进行数值最小化的方法。由于区间分析的性质,保证了该方法收敛到具有数学确定性和任意精度的全局解,并且只需要用户输入一组点对应和一个搜索框。代价函数基于基本矩阵的Huang-Faugeras约束。研究了最近提出的一种基于Bernstein多项式形式的区间扩展,以加快求解速度。最后给出了在合成图像上的一些实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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