Image-based characterization of the pulp flows
M. Sorokin, Nataliya Strokina, Tuomas Eerola, Lasse Lensu, Kyösti Karttunen, Heikki A. Kalviainen · Pattern Recognition and Image Analysis · 2016
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.