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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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