Adaptive Classification of Dirt Particles in Papermaking Process

  • Nataliya Strokina
  • Tuomas Eerola
  • Lasse Lensu
  • Heikki Kälviäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)

Abstract

In pulping and papermaking, dirt particles significantly affect the quality of paper. Knowledge of the dirt type helps to track the sources of the impurities which would considerably improve the paper making process. Dirt particle classification designed for this purpose should be adaptable because the dirt types are specific to the different processes of paper mills. This paper introduces a general approach for the adaptable classification system. The attention is paid to feature extraction and evaluation, in order to determine a suboptimal set of features for a certain data. The performance of standard classifiers on the provided data is presented, considering how the dirt particles or different types are classified. The effect of dirt particle grouping according to the particle size on the results of classification and feature evaluation is discussed. It is shown that the representative features of dirt particles from different size groups are different, which has an effect on the classification.

Keywords

machine vision particle segmentation dirt particle classification feature extraction pulping papermaking image processing and analysis 

References

  1. 1.
    Duarte, F., Araujo, H., Dourado, A.: An Automatic System for Dirt in Pulp Inspection Using Hierarchical Image Segmentation. Computers & Industrial Engineering 37, 343–346 (1999)CrossRefGoogle Scholar
  2. 2.
    Jones, S., Thomas, R., Awcock, G., Humphrey, K.: Machine vision techniques for ink particle analysis within the paper recycling process. In: Fifth International Conference on Image Processing and its Applications, pp. 682–686 (1995)Google Scholar
  3. 3.
    Parker, S., Chan, J.R.: Dirt Counting in Pulp: An Approach Using Image Analysis Methods. In: Proceedings of the IASTED International Conference on Signal and Image Processing, SIP (2002)Google Scholar
  4. 4.
    Fouladgaran, M., Mankki, A., Lensu, L., Käyhkö, J., Kälviäinen, H.: Automated Counting and Characterization of Dirt Particles in Pulp. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 166–174. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990)MATHGoogle Scholar
  6. 6.
    The Verity IA Color Image Analysis software. Dirt Counter, http://www.verityia.com/stripscanner.php
  7. 7.
    Kittler, J., Illingworth, J.: On threshold selection using clustering criteria. IEEE Transactions on System, Man, and Cybernetics 12, 652–655 (1985)CrossRefGoogle Scholar
  8. 8.
    Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)CrossRefGoogle Scholar
  9. 9.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (1999)MATHGoogle Scholar
  10. 10.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)CrossRefGoogle Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Chichester (2001)MATHGoogle Scholar
  12. 12.
    Freund, Y.: Boosting a weak learning algorithm by majority. Information and Computation 121(2), 256–285 (1995)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Pulps – Estimation of dirt and shives – Part 1: Inspection of laboratory sheets by transmitted light. ISO 5350-1:2006 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nataliya Strokina
    • 1
  • Tuomas Eerola
    • 1
  • Lasse Lensu
    • 1
  • Heikki Kälviäinen
    • 1
  1. 1.Machine Vision and Pattern Recognition Laboratory (MVPR), Department of Information TechnologyLappeenranta University of Technology (LUT)LappeenrantaFinland

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