Material Classification for Printed Circuit Boards by Spectral Imaging System

  • Abdelhameed Ibrahim
  • Shoji Tominaga
  • Takahiko Horiuchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5646)


This paper presents an approach to a reliable material classification for printed circuit boards (PCBs) by constructing a spectral imaging system. The system works in the whole spectral range [400-700nm] and the high spectral resolution. An algorithm is presented for effectively classifying the surface material on each pixel point into several elements such as substrate, metal, resist, footprint, and paint, based on the surface-spectral reflectance estimated from the spectral imaging data. The proposed approach is an incorporation of spectral reflectance estimation, spectral feature extraction, and image segmentation processes for material classification of raw PCBs. The performance of the proposed method is compared with other methods using the RGB-reflectance based algorithm, the k-means algorithm and the normalized cut algorithm. The experimental results show the superiority of our method in accuracy and computational cost.


Spectral imaging system material classification printed circuit board spectral reflectance region segmentation k-means normalized cut 


  1. 1.
    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
  2. 2.
    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)Google Scholar
  3. 3.
    Ibrahim, Z., Al-Attas, S.A.R.: Wavelet-based printed circuit board inspection algorithm. Integrated Computer-Aided Engineering 12, 201–213 (2005)Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley and Sons, New York (2001)zbMATHGoogle Scholar
  9. 9.
    Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  10. 10.
    Tominaga, S.: Surface Identification using the Dichromatic Reflection Model. IEEE Trans. PAMI 13, 658–670 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Abdelhameed Ibrahim
    • 1
  • Shoji Tominaga
    • 1
  • Takahiko Horiuchi
    • 1
  1. 1.Department of Information Science, Graduate School of Advanced Integration ScienceChiba UniversityJapan

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