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Constructing a neural system for surface inspection

På svenska

Author

  • Carl-Henrik Grunditz
  • Martin Walder
  • Lambert Spaanenburg

Editor

  • Jacek Malec

Summary, in English

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%.

Publishing year

2004

Language

English

Pages

68-73

Publication/Series

SAIS Workshop

Document type

Conference paper

Publisher

SAIS

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Conference name

Joint SAIS/SSLS Workshop, 2004

Conference date

2004-04-15 - 2004-04-16

Conference place

Lund, Sweden

Status

Published

Research group

  • DISKA/DO:PING