Material Classification for Printed Circuit Boards by Kernel Fisher Discriminant Analysis

This paper proposes an approach to a reliable material classification for printed circuit boards by kernel Fisher discriminant analysis. The proposed approach uses only three dimensional features of the surface-spectral reflectance reduced from the high-dimensional spectral imaging data for effectively classifying the surface material on each pixel point into several elements such as substrate, metal, resist, footprint, and silk-screen paint. We show that a linear classification of these elements does not work well, because the feature distribution is not well separated in the three dimensional feature space. In this paper, a kernel technique is used to constructs a subspace where the class separability is maximized in a high-dimensional feature space. The performance of the proposed method is compared with the previous algorithms using the high-dimensional spectral data.