Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

中文翻译：

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最大流量最小割算法在材料数据集分割和聚类中的应用。

涉及图像分割，原子团簇识别，图像中微结构成分的分割和奥氏体重建的问题已经看到了各种方法试图用混合结果来解决它们。由于它们之间的巨大差异，没有一种计算技术能够有效解决这些问题。我们建议将图形切割作为一种通用技术来应用，它可以为众多材料数据分析问题提供解决方案。这可以归因于其配置灵活性以及处理嘈杂的实验数据的能力。贝叶斯统计方法的实现允许基于实验结果并且已经根深蒂固在节点内的先验信息来驱动预期的解决方案。这条路，可以将图中的节点与相似的相邻节点组合在一起，然后根据计算出的似然将它们分配给特定系统。将概率与系统的潜在解决方案和状态相关联可以进行定量的随机分析。只要可以在模型系统中有效地建立节点网络，每个问题的有希望的稳健结果就表明该技术的潜在用途。