Machine learning offers attractive solutions to challenging image processing tasks. Tedious development and parametrization of algorithmic solutions can be replaced by training a convolutional neural network or a random forest with a high potential to generalize. However, machine learning methods rely on huge amounts of representative image data along with a ground truth, usually obtained by manual annotation. Thus, limited availability of training data is a critical bottleneck. We discuss two use cases: optical quality control in industrial production and segmenting crack structures in 3D images of concrete. For optical quality control, all defect types have to be trained but are typically not evenly represented in the training data. Additionally, manual annotation is costly and often inconsistent. It is nearly impossible in the second case: segmentation of crack systems in 3D images of concrete. Synthetic images, generated based on realizations of stochastic geometry models, offer an elegant way out. A wide variety of structure types can be generated. The within structure variation is naturally captured by the stochastic nature of the models and the ground truth is for free. Many new questions arise. In particular, which characteristics of the real image data have to be met to which degree of fidelity.

翻译：机器学习为具有挑战性的图像处理任务提供了极具吸引力的解决方案。繁琐的算法开发与参数化过程可被训练卷积神经网络或随机森林所替代，这些方法具备强大的泛化潜力。然而，机器学习方法依赖于大量具有真实标注的代表性图像数据，而真实标注通常通过人工标注获得。因此，训练数据的有限可用性成为关键瓶颈。本文探讨两个应用场景：工业生产中的光学质量检测，以及混凝土三维图像中裂缝结构的分割。对于光学质量检测，所有缺陷类型均需进行训练，但它们在训练数据中的分布通常不均。此外，人工标注成本高昂且常存在不一致性。在第二种场景——混凝土三维图像中裂缝系统的分割——中，人工标注几乎无法实现。基于随机几何模型实现生成的合成图像提供了一种优雅的解决方案。该方法可生成多样化的结构类型，其结构内部变异通过模型的随机性自然体现，且真实标注可自动获取。由此衍生出诸多新问题，特别是：真实图像数据的哪些特征需要被满足？需要达到何种保真度？