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)


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.


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


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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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