This work addresses segmentation of volumetric images of woven carbon fiber textiles from micro‐tomography data. We propose a semi‐supervised algorithm to classify carbon fibers that requires sparse input as opposed to completely labeled images. The main contributions are: (a) design of effective discriminative classifiers, for three‐dimensional textile samples, trained on wavelet features for segmentation; (b) coupling of previous step with nonlocal means as simple, efficient alternative to the Potts model; and (c) demonstration of reuse of classifier to diverse samples containing similar content. We evaluate our work by curating test sets of voxels in the absence of a complete ground truth mask. The algorithm obtains an average 0.95 F1 score on test sets and average F1 score of 0.93 on new samples. We conclude with discussion of failure cases and propose future directions toward analysis of spatiotemporal high‐resolution micro‐tomography images.

中文翻译：

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使用小波和非局部方法对纺织品显微断层扫描数据进行交互式体积分割

这项工作解决了从显微断层扫描数据对碳纤维编织物的体积图像进行分割的问题。我们提出了一种半监督算法来对碳纤维进行分类，该算法需要稀疏输入，而不是完全标记的图像。主要贡献是：（a）针对小尺寸特征训练的三维纺织品样本的有效区分器设计；（b）将上一步与非本地方法相结合，作为Potts模型的简单，有效替代方案；（c）演示将分类器重用于包含相似内容的各种样本。我们通过在没有完整的地面真相蒙版的情况下整理体素测试集来评估我们的工作。该算法在测试集上获得0.95的平均F1分数，在新样本上获得0.93的平均F1分数。