Prof. Dr. Claudia Redenbach
Neural networks are commonly used for image segmentation. Training a network requires a suitable amount of training data, that is, images along with the desired segmentation result. Manual annotation of images is common practice, but time consuming and error prone.
We focus on the segmentation of images of materials microstructures. In this context, we suggest to use synthetic training data. For their simulation, virtual microstructures are generated from stochastic geometry models. Then, a model for the imaging process is applied to simulate realistic images of the synthetic structures. Binary images of the model realizations yield a ground truth for the segmentation. The resulting pairs are then used to train the neural network. We present two examples of application: segmentation of cracks in µCT images of concrete and of FIB-SEM images of porous structures.
11.07.2024, Raum: G03-106, Zeit: 17:00
