Filter-based feature selection for rail defect detection (Articolo in rivista)

Type
Label
  • Filter-based feature selection for rail defect detection (Articolo in rivista) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s00138-004-0148-3 (literal)
Alternative label
  • C. Mandriota; M. Nitti; N. Ancona; E. Stella; A. Distante (2004)
    Filter-based feature selection for rail defect detection
    in Machine vision and applications
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • C. Mandriota; M. Nitti; N. Ancona; E. Stella; A. Distante (literal)
Pagina inizio
  • 179 (literal)
Pagina fine
  • 185 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 15 (literal)
Rivista
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Studi sui Sistemi Intelligenti per l'Automazione, ISSIA-CNR via amendola 122/D-I Bari Italy (literal)
Titolo
  • Filter-based feature selection for rail defect detection (literal)
Abstract
  • Over the last few years research has been oriented toward developing a machine vision system for locating and identifying, automatically, defects on rails. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail,construction conditions, and speed and/or frequency of trains using the rail. The aim of this paper is to present an experimental comparison among three filtering approaches, based on texture analysis of rail surfaces, to detect the presence/absence of a particular class of surface defects: corrugation. (literal)
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