A feature selection method for automated Visual Inspection Systems

Hugo Garcia, J. Rene Villalobos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Automated Visual Inspection (AVI) systems are nowadays considered essential in the assembly of Surface Mounted Devices (SMD). The general goal of this research centers on developing self-training AVI systems for the inspection of SMD components. In this paper, it is proposed a new feature selection methodology based on a stepwise variable selection. The procedure uses an estimation of the marginal Misclassification Error Rate (MER) as the figure of merit to introduce new features in the quadratic classifier used by the inspection system. This marginal error rate is estimated by using the densities of the conditional stochastic representations of the underlying quadratic discriminant function. In this paper we show that the application of the proposed methodology to the inspecting of SMD components results in significant savings of computational time in the estimation of classification error over the traditional simulation and crossvalidation methods.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE INDIN 2008
Subtitle of host publication6th IEEE International Conference on Industrial Informatics
Pages1371-1376
Number of pages6
DOIs
StatePublished - Oct 31 2008
EventIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics - Daejeon, Korea, Republic of
Duration: Jul 13 2008Jul 16 2008

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
ISSN (Print)1935-4576

Other

OtherIEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics
CountryKorea, Republic of
CityDaejeon
Period7/13/087/16/08

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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