Cross-channel co-occurrence matrices for robust characterization of surface disruptions in 2 1/2D rail image analysis

  • Authors:

  • Daniel Soukup;Reinhold Huber-Mörk

  • Affiliations:

  • Safety & Security Department Research Area Intelligent Vision Systems, Austrian Institute of Technology, Austria;Safety & Security Department Research Area Intelligent Vision Systems, Austrian Institute of Technology, Austria

  • Venue:
  • ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
  • Year:
  • 2012

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Abstract

We present a new robust approach to the detection of rail surface disruptions in high-resolution images by means of 21/2D image analysis. The detection results are used to determine the condition of rails as a precaution to avoid breaks and further damage. Images of rails are taken with color line scan cameras at high resolution of about 0.2 millimeters under specific illumination to enable 21/2D image analysis. Pixel locations fulfilling the anti-correlation property between two color channels are detected and integrated over regions of general background deviations using so called cross-channel co-occurrence matrices, a novel variant of co-occurrence matrices introduced as part of this work. Consequently, the detection of rail surface disruptions is achieved with high precision, whereas the unintentional elimination of valid detections in the course of false and irrelevant detection removal is reduced. In this regard, the new approach is more robust than previous methods.