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.

翻译：尽管环氧树脂的工艺变量会改变其力学性能，但在视觉上识别这类材料样品X射线图像的特征仍具有挑战性。为便于识别，我们首先近似计算不同种类环氧树脂X射线图像强度场的梯度幅值，进而利用深度学习从变换后的图像中发现最具代表性的特征。在通过求解逆问题以寻找异质材料样品判别特征的过程中，我们采用卷积神经网络早期层特征图的所有通道经奇异值分解得到的特征向量。虽然最强激活通道能够直观呈现特征，但在实际应用中此类特征往往不够鲁棒。另一方面，当网络容量或网络架构等变量发生变化时，特征图矩阵分解的左奇异向量几乎保持不变。本研究展示了高分类精度与特征鲁棒性。