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Efficient MCMC sampling with implicit shape representations

机译:具有隐式形状表示的有效MCMC采样

摘要

In an approach for sampling a distribution of segmentations of an image, the segmentations are based on a level set function. For each sample of the distribution, the sampling includes the following, for each of multiple iterations: randomly selecting a set of locations of the level set function, determining a domain of allowed perturbations of the level set function at the selected set of locations, and randomly accepting a perturbation of the level set function according to a criterion corresponding to a biased distribution over the domain of allowed perturbations of the level set function, wherein the bias is selected to increase a probability of accepting the perturbation. The sampling also includes determining the sample of the distribution of segmentations according to a perturbed level set function determined in a final iteration of the multiple iterations for the sample.
机译:在用于采样图像的分割的分布的方法中,分割基于水平集函数。对于分布的每个样本,对于多次迭代中的每一个,采样都包括以下内容:随机选择级别集合函数的一组位置,确定所选位置集合处级别集合函数的允许扰动的域,以及根据与在水平集函数的允许扰动的域上的偏差分布相对应的标准,随机地接受水平集函数的扰动,其中,选择偏置以增加接受扰动的可能性。采样还包括根据在针对该样本的多次迭代的最终迭代中确定的扰动水平集函数来确定分段分布的样本。

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