A1 Journal article (refereed), original research

Comparison of bubble detectors and size distribution estimators

Open Access publication

Publication Details

Authors: Ilonen Jarmo, Juránek Roman, Eerola Tuomas, Lensu Lasse, Dubská Markéta, Zemčík Pavel, Kälviäinen Heikki

Publisher: Elsevier

Publication year: 2018

Language: English

Related journal or series: Pattern Recognition Letters

Volume number: 101

Start page: 60

End page: 66

Number of pages: 7

ISSN: 0167-8655

eISSN: 1872-7344

JUFO level of this publication: 2

Digital Object Identifier (DOI): http://dx.doi.org/10.1016/j.patrec.2017.11.014

Open Access: Open Access publication


Detection, counting and characterization of bubbles, that is, transparent objects in a liquid, is important in many industrial applications. These applications include monitoring of pulp delignification and multiphase dispersion processes common in the chemical, pharmaceutical, and food industries. Typically the aim is to measure the bubble size distribution. In this paper, we present a comprehensive comparison of bubble detection methods for challenging industrial image data. Moreover, we compare the detection-based methods to a direct bubble size distribution estimation method that does not require the detection of individual bubbles. The experiments showed that the approach based on a convolutional neural network (CNN) outperforms the other methods in detection accuracy. However, the boosting-based approaches were remarkably faster to compute. The power spectrum approach for direct bubble size distribution estimation produced accurate distributions and it is fast to compute, but it does not provide the spatial locations of the bubbles. Selecting the most suitable method depends on the specific application.


Last updated on 2019-13-03 at 12:00