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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)

Abstract

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.

Keywords

Anomaly detection Metallic surfaces Reflectance RTI 

Notes

Acknowledgments

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.

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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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