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Probabilistic surface inference for industrial inspection planning

: Mohammadikaji, M.; Bergmann, S.; Irgenfried, J.; Beyerer, Jürgen; Dachsbacher, C.; Wörn, H.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
WACV 2017, IEEE Winter Conference on Applications of Computer Vision. Proceedings : 24-31 March 2017, Santa Rosa, California
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-4822-9
ISBN: 978-1-5090-4823-6
Winter Conference on Applications of Computer Vision (WACV) <17, 2017, Santa Rosa/Calif.>
Conference Paper
Fraunhofer IOSB ()

Optimized machine vision setups are a requirement for precise and efficient product quality assurance. As the design space is high-dimensional, a manual design requires a lot of engineering experience and experimental work, associated with high costs and often non-optimal results. For automatic evaluation, there are evaluation metrics such as measurement uncertainty and scan resolution to evaluate the quality of an inspection. However, it is not trivial how to combine different criteria to optimize the setup based on the inspection requirements. We propose to fuse the metrics through a probabilistic surface inference to quantify the amount of information gained by a specific setup configuration. To this end, the product surface is modeled by a random process and the problem is adapted to a Gaussian Process (GP) inference. We introduce a local inference based on the local surface orientation and propose a novel spectrum-based approach to determine the GP parameters based on the required inspection resolution. The inference results, based on simulations, are demonstrated for the inspection of a cylinder head.