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Characterization of measurement systems based on vision

The research in this area were oriented to the study and development of methodologies for the metrological characterization of measurement systems based on image acquisition, and to the design and development of measurement systems of this kind for specific applications. We conducted a study of the propagation of measurement uncertainty through the stages of image acquisition and development of a measurement system based on machine vision. The attention was focused, first, on the stage of acquisition of the image, with the development of a model able to determine the value of the uncertainty of a single element of the image with an evaluation of the uncertainty of "type B" (non-statistical, therefore applicable to a single grabbed image). This model accounts for typical influence quantities of the industrial environment and requires some knowledge of the coefficients, whose determination requires the solution of an optimization problem. For this purpose, an approximate solution has been proposed and experimentally verified, applicable also in an industrial environment. Subsequently, the research has been oriented to the study of the propagation of uncertainty through the cascade of the algorithms which determine measurements of specific lengths of objects. Particular interest was devoted to the characterization of industrial inspection systems based on stereoscopic vision, obtained through the development of a laboratory prototype of a measurement station stereo and the comparison of metrological performances obtainable with different methodologies. Together with the research aimed at the metrological characterization of stereoscopic measurement systems, a real stereo measurement system, previously developed for measuring dimensional parameters of extruded rubber products for the automotive industry, was also characterized.


  • Stereo reconstruction geometry
  • Comparison between a priori estimation of 3-D stereo reconstruction uncertainty and experimental statistical uncertainty


main references

  • R. Anchini, G. Di Leo, C. Liguori, A. Paolillo, "Metrological Characterization of a Vision-Based Measurement System for the Online Inspection of Automotive Rubber Profile", IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 1, January 2009, pp. 4-13, Digital Object Identifier: 10.1109/TIM.2008.2004979
  • G. Di Leo, C. Liguori, A. Paolillo, "Covariance propagation for the uncertainty estimation in stereo vision", IEEE Transactions on Instrumentation & Measurement, Vol. 60, No. 5, May 2011, pp. 1664-1673, http://dx.doi.org/10.1109/TIM.2011.2113070
  • G. Di Leo, A. Paolillo, "Uncertainty Evaluation of Camera Model Parameters", IEEE International Instrumentation and Measurement Technology Conference (I²MTC), Binjiang, Hangzhou, China, May 10-12, 2011, pp. 598-603, http://dx.doi.org/10.1109/IMTC.2011.5944307
  • M. De Santo, C. Liguori, A. Paolillo, A. Pietrosanto: "Standard Uncertainty Evaluation in Image-based Measurements", Measurement, 36 (2004) 347-358, http://dx.doi.org/10.1016/j.measurement.2004.09.011
  • R. Anchini, C. Liguori, A. Paolillo, "Evaluation of the Uncertainty of Edge Detector Algorithms", IEEE Transactions on Instrumentation and Measurements, vol.56, no.3, June 2007, pp. 681-688, http://dx.doi.org/10.1109/AMYEM.2006.1650753