Particle size characterisation of in-flow milling products by video image analysis using global features

Abstract

The objective of this work was to characterise the particle size of milling products by image analysis. Four classes of milling products were obtained by varying the roll gap of the second break roll of the mill. Images were acquired by using an in-flow imaging system implemented in the mill, and 1300 images were recorded for each class. Three methods of image analysis were investigated: morphological opening, constant grey level run lengths and grey level spatial interdependences. Discriminant analyses were applied to the data extracted from the images by the three methods in order to identify each class of milling product. More than 77% of the samples were correctly assigned to their group, both for the calibration and validation sets. The best results were obtained by applying morphological openings or by computing parameters from the co-occurrence matrices. The number of correct classifications rose to 81% of samples with only three variables selected for the opening curves and to 83% with three co-occurrence parameters.