Measurement Traceability and Uncertainty in Machine Vision Applications

During the past decades increasing use of machine vision in dimensional measurements has been seen. From a metrological view every serious measurement should be traceable to SI units and have a stated measurement uncertainty. The first step to ensure this is the calibration of the measurement instruments. Quality systems in manufacturing industry require traceable calibrations and measurements. This has lead to a good knowledge of measurement accuracy for traditional manual hand-held measurement instruments. The entrance of rather complex computerised machine vision instruments and optical coordinate measuring machines, at the production lines and measurement rooms, is a threat or at least a challenge, to the understanding of the accuracy of the measurement. Accuracies of algorithms for edge detection and camera calibration are studied in the field of machine vision, but uncertainty evaluations of complete systems are seldom seen. In real applications the final measurement uncertainty is affected by many factors such as illumination, edge effects, the operator, and non-idealities of the object to be measured. In this thesis the use of the GUM (Guide to the Expression of Uncertainty in Measurement) method is applied for the estimation of measurement uncertainty in two machine vision applications. The work is mainly limited to two-dimensional applications where a gray-scale camera is used. The described equipment for calibration of micrometers using machine vision is unique. The full evaluation of measurement uncertainty in aperture diameter measurements using an optical coordinate measuring machine is presented for the first time. In the presented applications the uncertainty budgets are very different. This confirms the conclusion, that a detailed uncertainty budget is the only way to achieve an understanding of the reliability of dimensional measurements in machine vision. Uncertainty budgets for the type of the two described machine vision applications have never previously been published.

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