Training of classifiers using virtual samples only

Abstract

This paper describes the training of classifiers entity based on virtual images, endered by a ray-tracing software. Two classifiers, a support vector machine and a polynomial classifier, are trained solely with virtual samples and used for classification of real samples. The objects to be distinguished are holes vs. garbage (non-holes) out of a set of hole candidates in images of flanges. We analysed the effect of different classifier parameters and manipulation of the virtual samples. Error rates of 1.6% on real test samples are achieved.