Comparison of Appearance-Based and Geometry-Based Bubble Detectors

Strokina, Nataliya; Juránek, Roman; Eerola, Tuomas; Lensu, Lasse; Zemčík, Pavel; Kälviäinen, Heikki

doi:10.1007/978-3-319-11331-9_73

Cited by 1 publication

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“…In terms of bubble detection the sliding window method demonstrated better results whereas the most precise volume estimate was produced by the CCA method. The detailed comparison of the two methods can be found in [26].…”

Section: Cca Performance On Pulp Suspension Imagesmentioning

confidence: 99%

Detection of bubbles as concentric circular arrangements

Strokina

Matas

Eerola

et al. 2016

Machine Vision and Applications

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The paper proposes a method for the detection of bubble-like transparent objects in a liquid. The detection problem is non-trivial since bubble appearance varies considerably due to different lighting conditions causing contrast reversal and multiple interreflections. We formulate the problem as the detection of concentric circular arrangements (CCA). The CCAs are recovered in a hypothesize-optimizeverify framework. The hypothesis generation is based on sampling from the partially linked components of the nonmaximum suppressed responses of oriented ridge filters, and is followed by the CCA parameter estimation. Parameter optimization is carried out by minimizing a novel cost-function. The performance was tested on gas dispersion images of pulp suspension and oil dispersion images. The mean error of gas/oil volume estimation was used as a performance criterion due to the fact that the main goal of the applications driving the research was the bubble volume estimation. The method achieved 28 and 13 % of gas and oil volume estimation errors correspondingly outperforming the OpenCV Circular Hough Transform in both cases and the WaldBoost detector in gas volume estimation.B Tuomas Eerola tuomas.eerola@lut.fi

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Section: Cca Performance On Pulp Suspension Imagesmentioning

confidence: 99%