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Journal of Sensors and Sensor Systems An open-access peer-reviewed journal
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Volume 6, issue 1
J. Sens. Sens. Syst., 6, 145–153, 2017
https://doi.org/10.5194/jsss-6-145-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Sensors and Measurement Systems 2016

J. Sens. Sens. Syst., 6, 145–153, 2017
https://doi.org/10.5194/jsss-6-145-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular research article 06 Apr 2017

Regular research article | 06 Apr 2017

Statistical characterization of evaluation strategies for fringe projection systems by means of a model-based noise prediction

Marc Fischer, Marcus Petz, and Rainer Tutsch Marc Fischer et al.
  • Institut für Produktionsmesstechnik, Technische Universität Braunschweig, Braunschweig, Germany

Abstract. For optical 3-D measurement systems, camera noise is the dominant uncertainty factor when optically cooperative surfaces are measured in a stable and controlled environment. In industrial applications repeated measurements are seldom executed for this kind of measurement system. This leads to statistically suboptimal results in subsequent evaluation steps as the important information about the quality of individual measurement points is lost. In this work it will be shown that this information can be recovered for phase measuring optical systems with a model-based noise prediction. The capability of this approach will be demonstrated exemplarily for a fringe projection system and it will be shown that this method is indeed able to generate an individual estimate for the spatial stochastic deviations resulting from image sensor noise for each measurement point. This provides a valuable tool for a statistical characterization and comparison of different evaluation strategies, which is demonstrated exemplarily for two different triangulation procedures.

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For optical measurement systems, camera noise is the dominant uncertainty factor when optically cooperative surfaces are measured in a stable environment. In this work it will be shown that the resulting spatial noise can be estimated by means of a noise model. The capability of this approach will be demonstrated for a fringe projection system. This provides a valuable tool for a statistical comparison of different evaluation strategies, which is shown for two different triangulation procedures.
For optical measurement systems, camera noise is the dominant uncertainty factor when optically...
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