Specification and inspection of surface imperfections on optical elements are standardized processes as defined by the standards ISO 10110-7 and ISO 14997 respectively. According to the latter, manual visual inspection is typically employed to assess surface imperfections on basic optical elements. However, operator-dependent measurement results are not desirable due to their lack of reproducibility and variation across operators. In this article, we describe and analyze a machine vision setup designed to mimic a human testers inspection process in an automated and objective way. Our setup consists of multiple cameras and LED light sources, both arranged on the surface of a hemisphere with the optical element to be inspected at its centre. Motion of the sample during the image acquisition phase can be avoided by use of individually controllable LED sources. The system is capable of acquiring a sparse pseudo BRDF (Bidirectional Reflectance Distribution Function) representation of imperfections. Thus, enabling discrimination of imperfection classes defined in the ISO standard, as shown by experiments. Image fusion and imperfection classification methodologies are discussed and the feasibility to discriminate between dust and surface imperfection by a stereo-vision approach is demonstrated. A comparative analysis with results from manual visual inspection for 20 optical elements of the same geometry is given which indicates a good agreement.
The trend towards computers with multiple processing units keeps going with no end in sight. Modern consumer
computers come with 2 - 6 processing units. Programming methods have been unable to keep up with this fast
development. In this paper we present a framework that uses a dataflow model for parallel processing: the Generic
Parallel Rapid Development Toolkit, GePaRDT. This intuitive programming model eases the concurrent usage
of many processing units without specialized knowledge about parallel programming methods and it's pitfalls.
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