Image-based characterization of the pulp flows


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Resumo

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.

Sobre autores

M. Sorokin

Machine Vision and Pattern Recognition Laboratory, School of Engineering Science

Email: nataliya.strokina@tut.fi
Finlândia, Lappeenranta, FI-53851

N. Strokina

Machine Vision and Pattern Recognition Laboratory, School of Engineering Science; Computer Vision Group, Department of Signal Processing

Autor responsável pela correspondência
Email: nataliya.strokina@tut.fi
Finlândia, Lappeenranta, FI-53851; Tampere, FI-33101

T. Eerola

Machine Vision and Pattern Recognition Laboratory, School of Engineering Science

Email: nataliya.strokina@tut.fi
Finlândia, Lappeenranta, FI-53851

L. Lensu

Machine Vision and Pattern Recognition Laboratory, School of Engineering Science

Email: nataliya.strokina@tut.fi
Finlândia, Lappeenranta, FI-53851

K. Karttunen

Cemis-Oulu, Unit of Measurement Technology

Email: nataliya.strokina@tut.fi
Finlândia, Kajaani, FI-87400

H. Kalviainen

Machine Vision and Pattern Recognition Laboratory, School of Engineering Science; School of Information Technology

Email: nataliya.strokina@tut.fi
Finlândia, Lappeenranta, FI-53851; Bandar Sunway, Selangor Darul Ehsan, 46150

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