A Top-Down Approach for Automatic Dropper Extraction in Catenary Scenes

  • Authors:

  • Caroline Petitjean;Laurent Heutte;Régis Kouadio;Vincent Delcourt

  • Affiliations:

  • LITIS, Université de Rouen, France 76801;LITIS, Université de Rouen, France 76801;LITIS, Université de Rouen, France 76801 and Direction de l'Innovation et de la Recherche SNCF, Paris Cedex 8, France 75379;Direction de l'Innovation et de la Recherche SNCF, Paris Cedex 8, France 75379

  • Venue:
  • IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
  • Year:
  • 2009

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Abstract

This paper presents an original system for the automatic detection of droppers in catenary staves. Based on a top-down approach, our system exploits a priori knowledge that are used to perform a reliable extraction of droppers. Experiments conducted on a significant database of real catenary stave images show some promising results on this very challenging machine vision application.