Machine learning methods can automate the design of
large parts of an image processing pipeline in automated optical
inspection (AOI) systems. However, these methods typically require
an annotated sample of the objects under inspection, and
creating such samples is still a manual and labor-intensive process.
Synthetic image acquisition (SIA) can fill the gap to automate
this step. SIA joins a physically-based image synthesis
pipeline and procedural modeling techniques to recreate a physical
image acquisition process. We show that, when the hardware
parameters of a system are known, SIA can be used to train a
classifier, which can then be used for the physical system. Timeconsuming
manual acquisition and labeling of a training sample
is no longer necessary. Evaluations in the domain of glass recycling
demonstrate that the SIA approach performs on par with a
classifier that was trained using a manually collected training set.