Analysis of compatibility between lighting devices and descriptive features using Parzen’s kernel: application to flaw inspection by artificial vision

Abstract

We present a supervised method, developed for industrial inspections by artificial vision, to obtain an adapted combination of descriptive features and a lighting device. This method must be implemented under real-time constraints and therefore a minimal number of features must be selected. The method is based on the assessment of the discrimination power of many descriptive features. The objective is to select the combination of descriptive features and lighting system best able to discriminate flawed classes from defect-free classes. In the first step, probability densities are computed for flawed and defect-free classes and for each tested combination. The discrimination power of the features can be measured using the computed probability of error. In the second step, we obtain a combination that gives a low probability of errors. This leads us to choose each feature individually and then build a multidimensional decision space. A concrete application of this method is presented on an industrial problem of flaw detection by artificial vision. The results are compared with those given by classical multiple discriminant analysis (MDA) to justify the use of our method.