Although the process variables of epoxy resins alter their mechanical properties, the visual identification of the characteristic features of X-ray images of samples of these materials is challenging. To facilitate the identification, we approximate the magnitude of the gradient of the intensity field of the X-ray images of different kinds of epoxy resins and then we use deep learning to discover the most representative features of the transformed images. In this solution of the inverse problem to finding characteristic features to discriminate samples of heterogeneous materials, we use the eigenvectors obtained from the singular value decomposition of all the channels of the feature maps of the early layers in a convolutional neural network. While the strongest activated channel gives a visual representation of the characteristic features, often these are not robust enough in some practical settings. On the other hand, the left singular vectors of the matrix decomposition of the feature maps, barely change when variables such as the capacity of the network or network architecture change. High classification accuracy and robustness of characteristic features are presented in this work.

中文翻译：

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使用深度学习特征的奇异值分解的环氧树脂 X 射线图像的可见指纹

尽管环氧树脂的工艺变量会改变其机械性能，但对这些材料样品的 X 射线图像特征特征的视觉识别具有挑战性。为了便于识别，我们对不同种类环氧树脂的 X 射线图像的强度场梯度的大小进行了近似，然后我们使用深度学习来发现变换图像中最具代表性的特征。在这个寻找特征特征以区分异质材料样本的逆问题的解决方案中，我们使用从卷积神经网络中早期层的特征图的所有通道的奇异值分解中获得的特征向量。虽然最强的激活通道给出了特征特征的可视化表示，在某些实际环境中，这些通常不够稳健。另一方面，特征图矩阵分解的左奇异向量在网络容量或网络架构等变量变化时几乎没有变化。在这项工作中提出了高分类精度和特征特征的鲁棒性。