MLESAC: A new robust estimator with application to estimating image geometry
MLESAC: A new robust estimator with application to estimating image geometry
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DOI：
10.1006/cviu.1999.0832
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发表时间：
2000-04-01
影响因子：
4.5
通讯作者：
Zisserman, A
中科院分区：
文献类型：
--
作者：
Torr, PHS;Zisserman, A
提出了一种基于点对应的多视点关系鲁棒估计方法。该方法包括两个部分。第一种是一种新的鲁棒估计器MLESAC，它是RANSAC估计器的推广。它采用与RANSAC相同的采样策略来生成推定解，但选择最大化似然而不仅仅是内点数量的解。该算法的第二部分是一个通用的方法，自动参数化这些关系，使用MLESAC的输出。多视图图像关系的困难在于参数之间通常存在非线性约束，使得优化成为困难的任务。参数化方法克服了非线性约束的困难，进行了约束优化。该方法是一般的，它的使用说明基本矩阵，图像-图像单应性，和二次变换的估计。合成和真实的图像的结果。它表明，该方法给出的结果等于或上级以前的方法，（C）2000年学术出版社。
A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. The second part of the algorithm is a general purpose method for automatically parameterizing these relations, using the output of MLESAC. A difficulty with multiview image relations is that there are often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of nonlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matrices, image-image homographies, and quadratic transformations. Results are given for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches, (C) 2000 Academic Press.