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首页> 外文会议>European Conference on Computer Vision(ECCV 2006) pt.1; 20060507-13; Graz(AT) >A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation
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A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

机译：非线性优化中相互信息方法的统一框架

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摘要

Many variants of MI exist in the literature. These vary primarily in how the joint histogram is populated. This paper places the four main variants of MI: Standard sampling, Partial Volume Estimation (PVE), In-Parzen Windowing and Post-Parzen Windowing into a single mathematical framework. Jacobians and Hessians are derived in each case. A particular contribution is that the non-linearities implicit to standard sampling and post-Parzen windowing are explicitly dealt with. These non-linearities are a barrier to their use in optimisation. Side-by-side comparison of the MI variants is made using eight diverse data-sets, considering computational expense and convergence. In the experiments, PVE was generally the best performer, although standard sampling often performed nearly as well (if a higher sample rate was used). The widely used sum of squared differences metric performed as well as MI unless large occlusions and non-linear intensity relationships occurred. The binaries and scripts used for testing are available online.
机译：MI中有许多变体。这些主要不同之处在于联合直方图的填充方式。本文将MI的四个主要变体：标准采样，部分体积估计（PVE），Par-Parzen窗口化和Par-Parzen窗口化放入单个数学框架中。每种情况都派生雅各布派和黑森派。一个特别的贡献是，显式处理了标准采样和后Parzen窗口隐含的非线性。这些非线性是它们在优化中使用的障碍。考虑到计算成本和收敛性，使用八个不同的数据集对MI变量进行了并排比较。在实验中，PVE通常是性能最好的，尽管标准采样通常也执行得差不多（如果使用更高的采样率）。除非出现大的遮挡和非线性强度关系，否则将使用广泛使用的平方差和度量以及MI。用于测试的二进制文件和脚本可在线获得。

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