Metrological Characterization of a Vision-Based Measurement System for the Online Inspection of Automotive Rubber Profile

This paper deals with the metrological characterization of a stereovision-based measurement system for the inspection of automotive rubber profiles in an industrial plant. The characterization of this class of measurement systems introduces new challenges due to both the unavailability of reference measurement instruments and the complexity of the measurement system itself, which does not allow a straightforward application of the standard procedures for uncertainty evaluation. To assign optimum values to a number of design parameters, the followed approach focuses not only on evaluating the total uncertainty but also on analyzing systematic effects and influence quantities.

[1]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[2]  Christine Connolly,et al.  Handbook of Image and Video Processing 2nd Edition (Hardback) , 2006 .

[3]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Pasquale Daponte,et al.  An image-based measurement system for the characterisation of automotive gaskets , 1999 .

[5]  Gregor Papa,et al.  Accuracy of a 3D reconstruction system , 2007 .

[6]  Alfredo Paolillo,et al.  NEW MEASUREMENT TECHNIQUES FOR THE ON LINE DIMENSION CHARACTERIZATION OF AUTOMOTIVE RUBBER PROFILES , 2006 .

[7]  Antonio Pietrosanto,et al.  Uncertainty evaluation in algorithms with conditional statement , 2004, IEEE Transactions on Instrumentation and Measurement.

[8]  Andrew M. Wallace,et al.  Industrial applications of computer vision since 1982 , 1988 .

[9]  Antonio Pietrosanto,et al.  Standard uncertainty evaluation in image-based measurements , 2004 .

[10]  C. Liguori,et al.  Metrological Characterization of a Vision-Based Measurement System for the Online Inspection of Automotive Rubber Profile , 2007, 2007 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement.

[11]  Mikko Sallinen,et al.  Recognition of large work objects in difficult industrial environments , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[12]  S. Standard GUIDE TO THE EXPRESSION OF UNCERTAINTY IN MEASUREMENT , 2006 .

[13]  J. J. Aguilar,et al.  Stereo vision for 3D measurement: accuracy analysis, calibration and industrial applications , 1996 .

[14]  C. Liguori,et al.  Evaluation of the Uncertainty of Edge Detector Algorithms , 2006, Proceedings of the 2006 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement (AMUEM 2006).

[15]  Antonio Pietrosanto,et al.  An on-line stereo-vision system for dimensional measurements of rubber extrusions , 2004 .

[16]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[17]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Olivier Faugeras,et al.  Three-Dimensional Computer Vision , 1993 .