Image analysis for microstructures based on stochastic models

The development of modern high-performance materials requires a deeper understanding of the complex relations between a material's microstructure geometry and its macroscopic properties. Analysis of three-dimensional image data combined with stochastic microstructure modelling is a promising approach to study these relations. Motivated by two typical application examples, a fibre reinforced polymer and a closed polymer foam, we introduce versatile model classes from stochastic geometry. We explain how their basic geometric characteristics can be estimated from tomographic image data. Finally, linking the estimated values to the model parameters, stochastic models are fitted to the observed microstructures.