  Vision systems, i.e., systems that allow to detect and track objects in
images, have gained substantial importance over the past decades. They are used
in quality assurance applications, e.g., for finding surface defects in
products during manufacturing, surveillance, but also automated driving,
requiring reliable behavior. Interestingly, there is only little work on
quality assurance and especially testing of vision systems in general. In this
paper, we contribute to the area of testing vision software, and present a
framework for the automated generation of tests for systems based on vision and
image recognition. The framework makes use of existing libraries allowing to
modify original images and to obtain similarities between the original and
modified images. We show how such a framework can be used for testing a
particular industrial application on identifying defects on riblet surfaces and
present preliminary results from the image classification domain.
