Automatic configuration of surface inspection systems

Automatic image processing, systems suffer from high engineering costs that are necessary to adapt the configuration of the system to the problem under consideration. As a consequence, this paper presents an approach to reduce these costs by applying optimization methods to the setup and the configuration of an image processing system. We report on experiments in which we automatically select adequate subsets of textural features from a large set of potential candidates. In addition we tell how and why training pattern selection is used as a part of the optimization process. Finally, we show how genetic programming can be used for the construction of new genetic feature sets. This method has no conventional pendant and offers an interesting way to complement the algorithms made by humans.