Email this article Image-based characterization of the pulp flows
- Authors: Sorokin M.1, Strokina N.1,2, Eerola T.1, Lensu L.1, Karttunen K.3, Kalviainen H.1,4
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Affiliations:
- Machine Vision and Pattern Recognition Laboratory, School of Engineering Science
- Computer Vision Group, Department of Signal Processing
- Cemis-Oulu, Unit of Measurement Technology
- School of Information Technology
- Issue: Vol 26, No 3 (2016)
- Pages: 630-637
- Section: Applied Problems
- URL: https://journals.rcsi.science/1054-6618/article/view/194880
- DOI: https://doi.org/10.1134/S1054661816030196
- ID: 194880
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Abstract
Material flow characterization is important in the process industries and its further automation. In this study, close-to-laminar pulp suspension flows are analyzed based on double-exposure images captured in laboratory conditions. The correlation-based methods including autocorrelation and the particle image pattern technique were studied. During the experiments, synthetic and real test data with manual ground truth were used. The particle image pattern matching method showed better performance achieving the accuracy of 90.0% for the real data set with linear motion of the suspension and 79.2% for the data set with flow distortions.
About the authors
M. Sorokin
Machine Vision and Pattern Recognition Laboratory, School of Engineering Science
Email: nataliya.strokina@tut.fi
Finland, Lappeenranta, FI-53851
N. Strokina
Machine Vision and Pattern Recognition Laboratory, School of Engineering Science; Computer Vision Group, Department of Signal Processing
Author for correspondence.
Email: nataliya.strokina@tut.fi
Finland, Lappeenranta, FI-53851; Tampere, FI-33101
T. Eerola
Machine Vision and Pattern Recognition Laboratory, School of Engineering Science
Email: nataliya.strokina@tut.fi
Finland, Lappeenranta, FI-53851
L. Lensu
Machine Vision and Pattern Recognition Laboratory, School of Engineering Science
Email: nataliya.strokina@tut.fi
Finland, Lappeenranta, FI-53851
K. Karttunen
Cemis-Oulu, Unit of Measurement Technology
Email: nataliya.strokina@tut.fi
Finland, Kajaani, FI-87400
H. Kalviainen
Machine Vision and Pattern Recognition Laboratory, School of Engineering Science; School of Information Technology
Email: nataliya.strokina@tut.fi
Finland, Lappeenranta, FI-53851; Bandar Sunway, Selangor Darul Ehsan, 46150
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