3D image based modelling for inspection of objects with micro-features, using inaccurate calibration patterns: an experimental contribution

  • Gianluca Percoco
  • Antonio José Sánchez Salmerón
Technical Paper


In this report, the authors give a contribution to the study of the influence of inaccuracy of the shape and the position of calibration targets, on the accuracy of Photogrammetry in digitizing micro-features. In a machine vision, camera calibration is performed with planar patterns printed on common papers. When the pattern is small, inaccuracies are experienced. This limitation is overcome by using a coordinate measuring machine to measure the pattern and improve the calibration data. After the calibration improvement, the photogrammetric results are comparable with the existing and more expensive accurate techniques. The use of photogrammetry or computer vision for measuring objects with micro features is a promising research field, yet unexplored. When calibrating cameras for small fields of view is gained, the inaccuracies of the pattern affect the calibration accuracy. A few research papers are available about camera calibration with inaccurate patterns, but no industrial approaches are presented to solve the problem with consequent industrial applications. In the present report, these issues are overcome with a practical method applied to two case studies.


3D Inspection Micro Photogrammetry Camera calibration Inaccurate pattern Image based modelling 



The first work-piece used as a case-study was kindly shared by Dr. Francesco Modica and Dr. Gianluca Trotta from the Institute for Industrial Technologies and Automation of the Italian National Research Council.


  1. 1.
    Raffaeli, R., Mengoni, M., Germani, M., Mandorli, F.: Off-line view planning for the inspection of mechanical parts. Int. J. Interact. Des. Manuf. 7, 1–12 (2013). doi: 10.1007/s12008-012-0160-1 CrossRefGoogle Scholar
  2. 2.
    Minguez, R., Arias, A., Etxaniz, O., Solaberrieta, E., Barrenetxea, L.: Framework for verification of positional tolerances with a 3D non-contact measurement method. Int. J. Interact. Des. Manuf., pp. 1–9 (2014) doi: 10.1007/s12008-014-0214-7
  3. 3.
    Chang, W.-Y., Tsai, C.-P.: Illumination characteristics and image stitching for automatic inspection of bicycle part. Assem. Autom. 34, 8–342 (2014). doi: 10.1108/AA-09-2013-076 CrossRefGoogle Scholar
  4. 4.
    Jayaweera, N., Webb, P., Johnson, C.: Measurement assisted robotic assembly of fabricated aero-engine components. Assem. Autom. 30, 56–65 (2010). doi: 10.1108/01445151011016073 CrossRefGoogle Scholar
  5. 5.
    Yukan, H., Yuan, L., Jie, Z., Tang, W.-B., Shoushan, J.: A simple mechanical measurement system for the posture evaluation of wing components using the PSO and ICP algorithms. Assem. Autom. 35, 13–104 (2015). doi: 10.1108/AA-03-2014-025 CrossRefGoogle Scholar
  6. 6.
    Maté González, M.Á, Yravedra, J., González-Aguilera. D., Palomeque-González, JF., Domínguez-Rodrigo, M.: Micro-photogrammetric characterization of cut marks on bones. J Archaeol Sci 62,128–42 (2015). doi: 10.1016/j.jas.2015.08.006
  7. 7.
    Rodríguez-martín, M., Lagüela, S., González-aguilera, D., Rodríguez-gonzálvez, P.: Optics and laser technology procedure for quality inspection of welds based on macro-photogrammetric three-dimensional reconstruction 73, 54–62 (2015)Google Scholar
  8. 8.
    Ricolfe-Viala, C., Sanchez-Salmeron, A.-J.: Camera calibration under optimal conditions. Opt. Express 19, 75–10769 (2011). doi: 10.1364/OE.19.010769 CrossRefGoogle Scholar
  9. 9.
    Percoco, G., Lavecchia, F., Salmerón, A.J.S.: Preliminary study on the 3D digitization of millimeter scale products by means of photogrammetry. Proc. CIRP 33, 62–257 (2015). doi: 10.1016/j.procir.2015.06.046 CrossRefGoogle Scholar
  10. 10.
    Galantucci, L.M., Pesce, M., Lavecchia, F.: A stereo photogrammetry scanning methodology, for precise and accurate 3D digitization of small parts with sub-millimeter sized features. CIRP Ann. Manuf. Technol. 64, 10–507 (2015). doi: 10.1016/j.cirp.2015.04.016
  11. 11.
    Albarelli, A., Rodolà, E., Torsello, A.: Robust camera calibration using inaccurate targets. Procedings Br. Mach. Vis. Conf. 2010, British Machine Vision Association, pp. 16.1–16.10 (2010). doi: 10.5244/C.24.16
  12. 12.
    Huang, L., Zhang, Q., Asundi, A.: Flexible camera calibration using not-measured imperfect target. Appl. Opt. 52, 86–6278 (2013). doi: 10.1364/AO.52.006278 Google Scholar
  13. 13.
    Strobl, K.H., Hirzinger, G.: More accurate pinhole camera calibration with imperfect planar target. 2011 IEEE Int. Conf. Comput. Vis. Work. (ICCV Work). IEEE, pp. 75–1068 (2011). doi: 10.1109/ICCVW.2011.6130369
  14. 14.
    Brown, D.C.: Close-range camera calibration. Photogramm. Eng. 37, 66–855 (1971)Google Scholar
  15. 15.
    Ricolfe-Viala, C., Sanchez-Salmeron, A.: Lens distortion models evaluation. Appl. Opt. 49, 28–5914 (2010). doi: 10.1364/AO.49.005914
  16. 16.
    Bradski, G.: The OpenCV Library. Dr Dobb’s J Softw Tools (2000)Google Scholar

Copyright information

© Springer-Verlag France 2016

Authors and Affiliations

  1. 1.Dipartimento di Meccanica, Matematica e ManagementPolitecnico di BariBariItaly
  2. 2.Departamento de Ingeniería de Sistemas y AutomáticaUniversitat Politécnica de ValenciaValenciaSpain

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