Testing a structural light vision software by genetic algorithms: estimating the worst-case behavior of volume measurement

Timo J. Mantere; Jarmo T. Alander

doi:10.1117/12.444216

5 October 2001 Testing a structural light vision software by genetic algorithms: estimating the worst case behavior of volume measurement

Timo J. Mantere, Jarmo T. Alander

Author Affiliations +

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444216
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

In this study we use genetic algorithms to generate test surfaces for a proposed structured light 3D vision system in order to estimate the worst case behavior of error tolerances. The object software evaluates surface profiles for measuring volumes of small objects attached on surfaces that are highly constrained while somewhat arbitrarily shaped. The test system tries to find, by using genetic algorithm search, the shape that results the highest relative error of volume. The parameters of the object system to be optimized include laser angle, image size, object step size, and the number of scan directions. The preliminary results seem to indicate that a genetic algorithm based approach is a beneficial aid in optical system design.

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Timo J. Mantere and Jarmo T. Alander "Testing a structural light vision software by genetic algorithms: estimating the worst case behavior of volume measurement", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444216

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