A machine learning approach to determine bubble sizes in foam at a transparent wall
Leon Knüpfer, Sascha Heitkam
Abstract
Abstract This article describes the use of a machine learning based technique to measure the bubble sizes of foam with polyhedral bubble shape in contact with a transparent wall. For two different experimental cases images are obtained of foam in a cylindrical column and labeled with a classical image processing algorithm. An available neural network based model, initially designed for cell image applications, is trained and validated to segment the images. When comparing the bubble size distribution in images found using the trained model with manually segmented images a good agreement over a large range of diameters can be found. The error of the mean diameter in both cases lies below <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>10</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> , mostly attributed to the failed recognition of tiny round bubbles in dry foam. The trained model is provided for further usage.