  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.
