Publication detail

Comparison of bubble detectors and size distribution estimators

KÄLVIÄINEN, H. EEROLA, T. LENSU, L. ILONEN, J. ZEMČÍK, P. JURÁNKOVÁ, M. JURÁNEK, R.

Original Title

Comparison of bubble detectors and size distribution estimators

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Bubble detection Size distribution estimation Boosting-based detection Convolutional neural networks (CNN) Pulping

Authors

KÄLVIÄINEN, H.; EEROLA, T.; LENSU, L.; ILONEN, J.; ZEMČÍK, P.; JURÁNKOVÁ, M.; JURÁNEK, R.

Released

1. 1. 2018

ISBN

0167-8655

Periodical

PATTERN RECOGNITION LETTERS

Year of study

101

Number

1

State

Kingdom of the Netherlands

Pages from

60

Pages to

66

Pages count

7

URL

BibTex

@article{BUT163408,
  author="KÄLVIÄINEN, H. and EEROLA, T. and LENSU, L. and ILONEN, J. and ZEMČÍK, P. and JURÁNKOVÁ, M. and JURÁNEK, R.",
  title="Comparison of bubble detectors and size distribution estimators",
  journal="PATTERN RECOGNITION LETTERS",
  year="2018",
  volume="101",
  number="1",
  pages="60--66",
  doi="10.1016/j.patrec.2017.11.014",
  issn="0167-8655",
  url="https://www.sciencedirect.com/science/article/pii/S0167865517304282"
}