Image-based characterization of the pulp flows

M. Sorokin; N. Strokina; T. Eerola; L. Lensu; K. Karttunen; H. Kalviainen

doi:10.1134/S1054661816030196

Image-based characterization of the pulp flows

M. Sorokin, N. Strokina, T. Eerola, L. Lensu, K. Karttunen, H. Kalviainen

Research output: Contribution to journal › Article › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	630-637
Number of pages	8
Journal	Pattern Recognition and Image Analysis
Volume	26
Issue number	3
DOIs	https://doi.org/10.1134/S1054661816030196
Publication status	Published - 1 Jul 2016
Publication type	A1 Journal article-refereed

Keywords

double-exposure
particle image velocimetry
pulp flow estimation

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Publication forum level 0

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition

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title = "Image-based characterization of the pulp flows",

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.",

keywords = "double-exposure, particle image velocimetry, pulp flow estimation",

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T1 - Image-based characterization of the pulp flows

AU - Sorokin, M.

AU - Strokina, N.

AU - Eerola, T.

AU - Lensu, L.

AU - Karttunen, K.

AU - Kalviainen, H.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - 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.

AB - 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.

KW - double-exposure

KW - particle image velocimetry

KW - pulp flow estimation

U2 - 10.1134/S1054661816030196

DO - 10.1134/S1054661816030196

M3 - Article

AN - SCOPUS:84984924424

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SP - 630

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JO - Pattern Recognition and Image Analysis

JF - Pattern Recognition and Image Analysis

IS - 3

ER -

Image-based characterization of the pulp flows

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Keywords

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