An automated feature selection method for visual inspection systems

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

Automated visual inspection (AVI) systems these days are considered essential in the assembly of surface-mounted devices (SMDs) in the electronics industry. This industry has faced the problem of rapid introduction and retirement of SMD-based products with the consequent obsolescence of the inspection systems already in the assembly lines. The constant introduction of new products has caused AVI systems to become rapidly obsolete. The general goal of this research centers on developing self-training AVI systems for the inspection of SMD components. The premise is that these systems would be less prone to obsolescence. In this paper, the authors describe the methodology being used for automatically selecting the features to inspect new components. In particular, this paper explores the use of multivariate stepwise discriminant analysis techniques, such as Wilks' Lambda, in order to automate the feature selection process. All of these techniques are applied to a case study of the inspection of SMD components.

Original languageEnglish (US)
Article number1707957
Pages (from-to)394-406
Number of pages13
JournalIEEE Transactions on Automation Science and Engineering
Volume3
Issue number4
DOIs
StatePublished - Oct 1 2006

Keywords

  • Automated visual inspection (AVI)
  • Feature selection
  • Quadratic vector classifier

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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