Advertisement

Discrimination of True Defect and Indefinite Defect with Visual Inspection Using SVM

  • Yuji Iwahori
  • Kazuya Futamura
  • Yoshinori Adachi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6884)

Abstract

This paper proposes a new approach to discriminate the true defect and the indefinite defect with visual distinction of the electronic board. Some classification approaches have been proposed for the limited kinds of defects and there may be some incorrect recognitions for the defect which is difficult with the visual distinction. This paper proposes the approach to reduce the incorrect recognition ratio for the defects with difficult discrimination using the margin of SVM. Real electronic board image data are tested and evaluated with the proposed approach.

Keywords

Feature Selection Reference Image Defect Region Margin Region Defect Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Roh, B., et al.: A Neural Network Approach to Defect Classification on Printed Circuit Boards. Journal of the Japan Society of Precision Engineering 67(10), 1621–1626 (2001) (in Japanese)Google Scholar
  2. 2.
    Tanaka, T., Hotta, S., Iga, T., Nakamura, T.: Automatic Image Filter Creation System: To Use for a Defect Classification System. IEICE Technical Report 106(448), 195–198 (2007)Google Scholar
  3. 3.
    Rau, H., Wu, C.-H.: Automatic Optical Inspection for Detecting Defects on Printed Circuit Board Inner Layers. The International Journal of Advanced Manufacturing Technology 25(9-10), 940–946 (2005)CrossRefGoogle Scholar
  4. 4.
    Kondo, K., et al.: Defect Classification Using Random Feature Selection and Bagging. The Journal of the Institute of Image Electronics Engineers of Japan 38(1), 9–15 (2009) (in Japanese)Google Scholar
  5. 5.
    Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yuji Iwahori
    • 1
  • Kazuya Futamura
    • 1
  • Yoshinori Adachi
    • 2
  1. 1.Dept. of Computer ScienceChubu UniversityKasugaiJapan
  2. 2.College of Business Admin. and Info. Sci.Chubu UniversityKasugaiJapan

Personalised recommendations