Automated recognition of oil drops in images of multiphase dispersions via gradient direction pattern

Domínguez, A.; Corkidi, Gabriel

doi:10.1109/cisp.2011.6100481

Cited by 4 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CCA detection method was tested on two sets of data: (i) pulp suspension images to estimate the gas volume contained in the bubbles and (ii) oil dispersion images [10]. The performance of the CCA method, standard OpenCV HT and sliding window method with WaldBoost detector [24] were compared.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…In the method, false positives were manually removed after the HT. The work was continued in [10] where the focus was on the detection of oil drops in dispersion images and automatic elimination of the false positives. Since the oil drops were not the only circular objects in the images, the authors designed a feature to distinguish between the oil drops and other objects.…”

Section: Geometry-based Approachesmentioning

confidence: 99%

“…The second dataset was provided by Dominguez et al [10] and included 236 images (all used as test set) with the resolution of 319×240 containing 1114 oil drops marked by an expert. The method parameters were the same as in the pulp suspension experiments, but the maximum radius value was set to 30 as the maximum radius of the ground truth bubbles and the segmentation threshold was set to 0.7 selected based on the method performance.…”

Section: Cca Performance On Oil Dispersion Imagesmentioning

confidence: 99%

“…The original work [10] focused on the detection of the oil drops only. The images contain also other circular objects such as air bubbles, but the provided GT contained only the parameters of the oil drops.…”

Section: Cca Performance On Oil Dispersion Imagesmentioning

confidence: 99%

“…The detection of bubbles, or more generally, transparent roughly spherical objects in a liquid is important for several machine vision applications, for example, dispersion of oil drops in water [10], air bubble detection in dense dispersion [34], and air bubble segmentation from the high-speed video acquired by equipment of multi-phase flow device [30]. In this paper, the research is driven by the pulp making industry.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Detection of bubbles as concentric circular arrangements

Strokina

Matas

Eerola

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

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

show abstract

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Geometry-based Approachesmentioning

confidence: 99%

Section: Cca Performance On Oil Dispersion Imagesmentioning

confidence: 99%

Section: Cca Performance On Oil Dispersion Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of bubbles as concentric circular arrangements

Strokina

Matas

Eerola

et al. 2016

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

Gradient‐Direction‐Pattern Transform for Automated Measurement of Oil Drops in Images of Multiphase Dispersions

et al. 2014

View full text Add to dashboard Cite

The automated detection and measurement of oil drops in multiphase fermentation systems are important for mass transfer analysis. A novel computer technique for automated detection of oil drops in images is presented in the context of a stirred tank containing a three‐phase water‐oil‐air dispersion. The technique is an original feature extraction transform designed for the detection of objects with a characteristic appearance. The proposed transform, denominated gradient‐direction‐pattern (GDP) transform, utilizes naturally occurring patterns in the orientation of the local gradient appearing in test images. The GDP transform was used to demonstrate the feasibility of automatically estimating oil drop‐size unbiased distributions which is an important task in the chemical and other related industries.

show abstract