Material Classification for Printed Circuit Boards by Kernel Fisher Discriminant Analysis

  • Takahiko Horiuchi
  • Yuhei Suzuki
  • Shoji Tominaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)


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.


Material classification printed circuit board spectral reflectance region segmentation kernel discriminant analysis 


  1. 1.
    Moganti, F., Ercal, F., Dagli, C.H., Tsunekawa, S.: Automatic PCB Inspection Algorithms: A Survey. Computer Vision and Image Understanding 63(2), 287–313 (1996)CrossRefGoogle Scholar
  2. 2.
    Chang, P.C., Chen, L.Y., Fan, C.Y.: A Case-based Evolutionary Model for Defect Classification of Printed Circuit Board Images. J. Intell. Manuf. 19, 203–214 (2008)CrossRefGoogle Scholar
  3. 3.
    Tsai, D.M., Yang, R.H.: An Eigenvalue-based Similarity Measure and Its Application in Defect Detection. Image and Vision Computing 23(12), 1094–1101 (2005)CrossRefGoogle Scholar
  4. 4.
    Ibrahim, Z., Al-Attas, S.A.R.: Wavelet-based Printed Circuit Board Inspection Algorithm. Integrated Computer-Aided Engineering 12, 201–213 (2005)Google Scholar
  5. 5.
    Huang, S.Y., Mao, C.W., Cheng, K.S.: Contour-Based Window Extraction Algorithm for Bare Printed Circuit Board Inspection. IEICE Trans. 88-D, 2802–2810 (2005)CrossRefGoogle Scholar
  6. 6.
    Leta, F.R., Feliciano, F.F., Martins, F.P.R.: Computer Vision System for Printed Circuit Board Inspection. In: ABCM Symp. Series in Mechatronics, vol. 3, pp. 623–632 (2008)Google Scholar
  7. 7.
    Tominaga, S.: Material Identification via Multi-Spectral Imaging and Its Application to Circuit Boards. In: 10th Color Imaging Conference, Color Science, Systems and Applications, Scottsdale, Arizona, pp. 217–222 (2002)Google Scholar
  8. 8.
    Tominaga, S., Okamoto, S.: Reflectance-Based Material Classification for Printed Circuit Boards. In: 12th Int. Conf. on Image Analysis and Processing, Italy, pp. 238–243 (2003)Google Scholar
  9. 9.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: Material Classification for Printed Circuit Boards by Spectral Imaging System. In: Trémeau, A., Schettini, R., Tominaga, S. (eds.) CCIW 2009. LNCS, vol. 5646, pp. 216–225. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Ibrahim, A., Tominaga, S., Horiuchi, T.: A Spectral Imaging Method for Material Classification and Inspection of Printed Circuit Boards. Optical Engineering 49(5), 057201-1–057201-10 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Takahiko Horiuchi
    • 1
  • Yuhei Suzuki
    • 1
  • Shoji Tominaga
    • 1
  1. 1.Graduate School of Advanced Integration ScienceChiba UniversityJapan

Personalised recommendations