Comparison of bubble detectors and size distribution estimators
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F18%3APU136060" target="_blank" >RIV/00216305:26230/18:PU136060 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0167865517304282" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167865517304282</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patrec.2017.11.014" target="_blank" >10.1016/j.patrec.2017.11.014</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of bubble detectors and size distribution estimators
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PATTERN RECOGNITION LETTERS
ISSN
0167-8655
e-ISSN
1872-7344
Volume of the periodical
101
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
7
Pages from-to
60-66
UT code for WoS article
000418101400009
EID of the result in the Scopus database
2-s2.0-85035027807