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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.

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This paper proposes a new feature selection methodology. The methodology is based on the stepwise variable selection procedure, but, instead of using the traditional discriminant metrics such as Wilks' Lambda, it uses an estimation of the misclassification error as the figure of merit to evaluate the introduction of new features. The expected misclassification error rate (MER) is obtained by using the densities of a constructed function of random variables, which is the stochastic representation of the conditional distribution of the quadratic discriminant function estimate. The application of the proposed methodology results in significant savings of computational time in the estimation of classification error over the traditional simulation and cross-validation methods. One of the main advantages of the proposed method is that it provides a direct estimation of the expected misclassification error at the time of feature selection, which provides an immediate assessment of the benefits of introducing an additional feature into an inspection/classification algorithm.

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