Skip to main content
SpringerLink
Log in

Automatic Character Detection System for IC Test Handler Based on Active Learning SVM

  • Conference paper
  • First Online:
Intelligent Computing, Networked Control, and Their Engineering Applications (ICSEE 2017, LSMS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 762))

  • 2002 Accesses

Abstract

An automatic character detection system for IC test handler is designed to recognize the characters on the surface of IC chip based on active learning SVM. Firstly, industrial camera is employed to collect a large number of chips’ surface image. Secondly, image preprocessing is carried out, including image grayscale, binarization and filter processing. Thirdly, the features of the preprocessed image are extracted. To reduce the annotation cost for training data and improve recognition rate, active learning algorithm is used to label the training data, while support vector machine algorithm is used to classify those data. Comparison with SVM algorithm, template matching and BP neural network shows the effectiveness of the proposed algorithm.

Z. Gao—This work is supported by National Natural Science Foundation (NNSF) of China under Grant 31570998. Mechatronics Engineering Innovation Group project from Shanghai Education Commission and Shanghai Key Laboratory of Power Station Automation Technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tummala, R., Rymaszewski, E.J., Klopfenstein, A.G.: Microelectronics Packaging Handbook: Technology Drivers. Springer Science & Business Media, Heidelberg (2012)

    Google Scholar 

  2. Chen, Y.M., Chiang, J.-H.: Recognition of the making on integrated circuit chips based on the hybrid Fourier-AFMT. Glob. J. Comput. Sci. Technol. 10(2), 64–70 (2010)

    Google Scholar 

  3. Blaes, P., Young, C.: Mobile IC package recognition. Technical report, EE368 Project, Stanford University (2008)

    Google Scholar 

  4. Chen, S.-H., Liao, T.-T.: An automated IC chip marking inspection system for surface mounted devices on taping machines. J. Sci. Ind. Res. 68(5), 361–366 (2009)

    Google Scholar 

  5. Nagarajan, R., Yaacob, S., Pandian, P., et al.: A real time marking inspection scheme for sem-iconductor industries. Int. J. Adv. Manuf. Technol. 34(9), 926–932 (2007)

    Article  Google Scholar 

  6. Aghaie, M., Shokri, F., Tabari, M.Y.Z.: Automatic iranian vehicle license plate recognition system based on Support Vector Machine (SVM) algorithms. Comput. Appl. Eng. Educ. 2(1), 161–174 (2013)

    Google Scholar 

  7. Schölkopf, B., Smola, A.J., Williamson, R.C., et al.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)

    Article  Google Scholar 

  8. Schohn, G., Cohn, D.: Less is more: active learning with support vector machines. In: ICML, pp. 839–846 (2000)

    Google Scholar 

  9. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Upper Saddle River (2011)

    MATH  Google Scholar 

  10. Zhou, H., Li, X., Schaefer, G.: Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Underst. 117(9), 1004–1016 (2013)

    Article  Google Scholar 

  11. Zhang, K., Croookes, D., Fei, M.: Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy. In: BMVC (2016)

    Google Scholar 

  12. Aggarwal, N., Karl, W.C.: Line detection in images through regularized Hough transform. IEEE Trans. Image Process. 15(3), 582–591 (2006)

    Article  Google Scholar 

  13. Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  14. Guo, H., Wang, W.: An active learning-based SVM multi-class classifica-tion model. Pattern Recogn. 48(5), 1577–1597 (2015)

    Article  Google Scholar 

  15. Vladimir, N.V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)

    Article  Google Scholar 

  16. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2(11), 45–66 (2001)

    MATH  Google Scholar 

  17. Amari, S., Si, W.: Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 12(6), 783–789 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, People’s Republic of China

    Tianshan Wang, Fan Jiang, Xiaojin Zhu, Hesheng Zhang & Zhiyuan Gao

Authors
  1. Tianshan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaojin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hesheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiyuan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiyuan Gao .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, China

    Dong Yue

  2. Shanghai University , Shanghai, China

    Chen Peng

  3. Shanghai University , Shanghai, China

    Dajun Du

  4. Nanjing University of Posts and Telecommunications, Nanjing, China

    Tengfei Zhang

  5. Shanghai University , Shanghai, China

    Min Zheng

  6. Swinburne University of Technology, Melbourne, Victoria, Australia

    Qinglong Han

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Wang, T., Jiang, F., Zhu, X., Zhang, H., Gao, Z. (2017). Automatic Character Detection System for IC Test Handler Based on Active Learning SVM. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_34

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6373-2_34

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6372-5

  • Online ISBN: 978-981-10-6373-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation