Image formation simulation for computer-aided inspection planning of machine vision systems

Stephan Irgenfried; Stephan Bergmann; Mahsa Mohammadikaji; Jürgen Beyerer; Carsten Dachsbacher; Heinz Wörn

doi:10.1117/12.2269166

26 June 2017 Image formation simulation for computer-aided inspection planning of machine vision systems

Stephan Irgenfried, Stephan Bergmann, Mahsa Mohammadikaji, Jürgen Beyerer, Carsten Dachsbacher, Heinz Wörn

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 1033406 (2017) https://doi.org/10.1117/12.2269166
Event: SPIE Optical Metrology, 2017, Munich, Germany

Abstract

In this work, a simulation toolset for Computer Aided Inspection Planning (CAIP) of systems for automated optical inspection (AOI) is presented along with a versatile two-robot-setup for verification of simulation and system planning results. The toolset helps to narrow down the large design space of optical inspection systems in interaction with a system expert. The image formation taking place in optical inspection systems is simulated using GPU-based real time graphics and high quality off-line-rendering. The simulation pipeline allows a stepwise optimization of the system, from fast evaluation of surface patch visibility based on real time graphics up to evaluation of image processing results based on off-line global illumination calculation. A focus of this work is on the dependency of simulation quality on measuring, modeling and parameterizing the optical surface properties of the object to be inspected. The applicability to real world problems is demonstrated by taking the example of planning a 3D laser scanner application. Qualitative and quantitative comparison results of synthetic and real images are presented.

Citation Download Citation

Stephan Irgenfried, Stephan Bergmann, Mahsa Mohammadikaji, Jürgen Beyerer, Carsten Dachsbacher, and Heinz Wörn "Image formation simulation for computer-aided inspection planning of machine vision systems", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 1033406 (26 June 2017); https://doi.org/10.1117/12.2269166

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