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Improved fault detection for inline optical inspections by evaluation of NIR images

: Eigenbrod, Hartmut

Volltext urn:nbn:de:0011-n-2671515 (757 KByte PDF)
MD5 Fingerprint: ba601335f05a77593bd71d44ae435c8f
Erstellt am: 28.11.2013

Beyerer, Jürgen (Ed.); Puente Leon, Fernando (Ed.); Längle, Thomas (Ed.):
OCM 2013, Optical characterization of materials. Conference proceedings : Held in Karlsruhe, Germany from March 6 - 7, 2013
Karlsruhe: KIT Scientific Publishing, 2013
ISBN: 978-3-86644-965-7
ISBN: 978-3-86644-956-7 (Falsche ISBN!)
DOI: 10.5445/KSP/1000032143
Conference "Optical Characterization of Materials" (OCM) <2013, Karlsruhe>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPA ()
Near Infrared Sensor Systems (NIR); quality inspection; machine vision; maschinelles Sehen; surface contamination; Textilfertigung; Automatisches Prüfen; Optisches Messverfahren; Bildverarbeitung; Qualitätsprüfung; Polyester

Industrial image processing is a widespread technology to evaluate the quality of work pieces. Major advantages of this technology are its contact-free mode of operation, fast evaluation times and moderate system costs. Usually the visible part of the light spectrum is used to discriminate between good and bad work pieces. However, in several applications a combination or even a substitution with the near infrared (NIR) part of the spectrum advances the evaluation. In this paper, several real world applications are presented and the achieved improvements are summarized by showing qualitative and quantitative results.