Abstract
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.
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Horiuchi, T., Suzuki, Y., Tominaga, S. (2011). Material Classification for Printed Circuit Boards by Kernel Fisher Discriminant Analysis. In: Schettini, R., Tominaga, S., Trémeau, A. (eds) Computational Color Imaging. CCIW 2011. Lecture Notes in Computer Science, vol 6626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20404-3_12
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DOI: https://doi.org/10.1007/978-3-642-20404-3_12
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