TY - GEN
T1 - A feature selection method for automated Visual Inspection Systems
AU - Garcia, Hugo
AU - Villalobos, J. Rene
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=54849424759&partnerID=8YFLogxK
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U2 - 10.1109/INDIN.2008.4618318
DO - 10.1109/INDIN.2008.4618318
M3 - Conference contribution
AN - SCOPUS:54849424759
SN - 9781424421718
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 1371
EP - 1376
BT - Proceedings - IEEE INDIN 2008
T2 - IEEE INDIN 2008: 6th IEEE International Conference on Industrial Informatics
Y2 - 13 July 2008 through 16 July 2008
ER -