Robust Anomaly Detection Using Reflectance Transformation Imaging for Surface Quality Inspection

  • Gilles Pitard
  • Gaëtan Le Goïc
  • Alamin Mansouri
  • Hugues Favrelière
  • Maurice Pillet
  • Sony George
  • Jon Yngve Hardeberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10269)


We propose a novel methodology for the detection and analysis of visual anomalies on challenging surfaces (metallic). The method is based on a local assessment of the reflectance across the inspected surface, using Reflectance Transformation Imaging data: a set of luminance images captured by a fixed camera while varying light spatial positions. The reflectance, in each pixel, is modelled by means of a projection of the measured luminances onto a basis of geometric functions, in this case, the Discrete Modal Decomposition (DMD) basis. However, a robust detection and analysis of surface visual anomalies requires that the method must not be affected neither by the geometry (sensor and surface orientation) nor by the texture pattern orientation of the inspected surface. We therefore introduce a rotation-invariant representation on the DMD, from which we devise saliency maps representing the local differences on reflectances. The methodology is tested on different engineering metallic samples exhibiting several types of defects. Compared to other saliency assessments, the results of our methodology demonstrate the best performance regarding anomaly detection, localisation and analysis.


Anomaly detection Metallic surfaces Reflectance RTI 



The authors would like to warmly thank the Regional Research Council (RFF-Innlandet, Norway) and the partners of the MeSurA project (Measuring Surface Appearance) for their support.


  1. 1.
    Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. EURASIP J. Image Video Process. 2014(1), 50 (2014)CrossRefGoogle Scholar
  2. 2.
    Wu, G., Kwak, H., Jang, S., Xu, K., Xu, J.: Design of online surface inspection system of hot rolled strips. In: 2008 IEEE International Conference on Automation and Logistics (ICAL), pp. 2291–2295 (2008)Google Scholar
  3. 3.
    Reynolds, R.L., Karpala, F., Clarke, D.A., Hageniers, O.L.: Theory and applications of a surface inspection technique using double-pass retroreflection. Optical Eng. 32(9), 2122–2129 (1993)CrossRefGoogle Scholar
  4. 4.
    Heida, J.H., Bruinsma, A.J.A.: D-sight technique for rapid impact damage detection on composite aircraft structures. In: Proceedings of the 7th European Conference on Non-Destructive Testing, pp. 1–12 (1998)Google Scholar
  5. 5.
    Earl, G., Martinez, K.: Archaeological applications of polynomial texture mapping: analysis, conservation and representation. J. Archaeol. Sci. 37(8), 2040–2050 (2010)CrossRefGoogle Scholar
  6. 6.
    Pitard, G., Le Goïc, G., Favreliere, H., Samper, S., Desage, S., Pillet, M.: Discrete modal decomposition for surface appearance modelling and rendering. In: SPIE Optical Metrology, vol. 9525, pp. 952523–952523-10 (2015)Google Scholar
  7. 7.
    Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., Limperis, T.: Geometrical considerations and nomenclature for Reflectance. Institute for Basic Standards, National Bureau of Standards, Washington (1977)Google Scholar
  8. 8.
    Malzbender, T., Gelb, D., Wolters, H.: Polynomial texture maps. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques (2001)Google Scholar
  9. 9.
    Dellepiane, M., Corsini, M., Callieri, M., Scopigno, R.: High quality PTM acquisition: reflection transformation imaging for large objects. In: VAST (2006)Google Scholar
  10. 10.
    Selmo, D., Sturt, F., Miles, J., Basford, P., Malzbender, T., Martinez, K., Thompson, C., Earl, G., Bevan, G.: Underwater reflectance transformation imaging: a technology for in situ underwater cultural heritage object-level recording. J. Electron. Imag. 26, 011029 (2017)CrossRefGoogle Scholar
  11. 11.
    Drew, M.S., Hel-Or, Y., Malzbender, T., Hajari, N.: Robust estimation of surface properties and interpolation of shadow/specularity components. Image Vis. Comput. 30(4–5), 317–331 (2012)CrossRefGoogle Scholar
  12. 12.
    Zhang, M., Drew, M.S.: Efficient robust image interpolation and surface properties using polynomial texture mapping. EURASIP J. Image Video Process. 2014(1), 1–19 (2014)CrossRefGoogle Scholar
  13. 13.
    Pitard, G.: Surface appearance metrology and modeling for industrial quality inspection. Ph.D. thesis, Université Grenoble Alpes (2016)Google Scholar
  14. 14.
    Mahalanobis, P.C.: On the generalized distance in statistics. In: Proceedings of the National Institute of Sciences (Calcutta) (1936)Google Scholar
  15. 15.
    Puntous, T., Pavan, S., Delafosse, D., Jourlin, M., Rech, J.: Ability of quality controllers to detect standard scratches on polished surfaces. Precis. Eng. 37(4), 924–928 (2013)CrossRefGoogle Scholar
  16. 16.
    Fecteau, J.H., Munoz, D.P.: Exploring the consequences of the previous trial. Nat. Rev. Neurosci. 4(6), 435–443 (2003)CrossRefGoogle Scholar
  17. 17.
    Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vis. Res. 40(10–12), 1489–1506 (2000)CrossRefGoogle Scholar
  18. 18.
    Walther, D., Koch, C.: Saliency Toolbox 2.3 (2006).
  19. 19.
    Itti, L., Dhavale, N., Pighin, F.: Realistic avatar eye and head animation using a neurobiological model of visual attention. In: SPIE’s 48th Annual Meeting on Optical Science and Technology, vol. 5200, pp. 64–78, January 2004Google Scholar
  20. 20.
    Lee, C.H., Varshney, A., Jacobs, D.W., Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Trans. Graph. (TOG) 24, 659–666 (2005)CrossRefGoogle Scholar
  21. 21.
    Le Moan, S., Mansouri, A., Hardeberg, J.Y., Voisin, Y.: Saliency for spectral image analysis. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 6(6), 2472–2479 (2013)CrossRefGoogle Scholar
  22. 22.
    Clarke, A.D.F., Green, P.R., Chantler, M.J., Emrith, K.: Visual search for a target against a 1/f\(\beta \) continuous textured background. Vis. Res. 48(21), 2193–2203 (2008)CrossRefGoogle Scholar
  23. 23.
    Clarke, A.D.F., Chantler, M.J., Green, P.R.: Modeling visual search on a rough surface. J. Vis. 9(4), 11.1–11.12 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gilles Pitard
    • 1
  • Gaëtan Le Goïc
    • 2
  • Alamin Mansouri
    • 2
  • Hugues Favrelière
    • 3
  • Maurice Pillet
    • 3
  • Sony George
    • 1
  • Jon Yngve Hardeberg
    • 1
  1. 1.The Norwegian Colour and Visual Computing Laboratory, Department of Computer Science, NTNUGjøvikNorway
  2. 2.Laboratoire LE2I, FRE CNRS 2005, UBFCAuxerreFrance
  3. 3.Laboratoire SYMME, EA 4144, USMBAnnecyFrance

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