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Constructing a neural system for surface inspection

Författare:
  • Carl-Henrik Grunditz
  • Martin Walder
  • Lambert Spaanenburg
Redaktör:
Publiceringsår: 2004
Språk: Engelska
Sidor: 68-73
Publikation/Tidskrift/Serie: SAIS Workshop
Dokumenttyp: Konferensbidrag
Förlag: SAIS

Sammanfattning

Visual quality assurance techniques focus on the detection and qualification of abnormal structures in the image of an object. The features of abnormality are extracted through image mining, whereupon classification is performed on characteristic combinations. Many techniques for feature extraction have been proposed, but the feed-forward neural network is seldom utilized despite its popularity in other application areas. Based on this wide experience base, this paper shows how a multi-tier feed-forward network can be constructed to model detectable peaks using only the physical properties of the image domain. This generic architecture can easily be adapted for different applications, as in metal plate inspection and protein detection, with mean error rate below 5%.

Disputation

Nyckelord

  • Technology and Engineering

Övriga

SAIS-SSLS Joint Workshop
2004-04-15/16
Lund
Published
  • DISKA/DO:PING

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