Template-based bubble identification and tracking in image sequences

Cheng, Da‐Chuan; Burkhardt, Hans

doi:10.1016/j.ijthermalsci.2004.08.008

Cited by 18 publications

(10 citation statements)

References 7 publications

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“…Because of the imaging conditions (see above), the bubble box also requires a high speed camera, such as in (Cheng and Burkhardt, 2006) (Leifer et al, 2003). Both requirements are costly and unwieldy in expedition use.…”

Section: Cameramentioning

confidence: 99%

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Zelenka

Koch

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Marine gas seeps, such as in the Panarea area near Sicily (McGinnis et al., 2011), emit large amounts of methane and carbon-dioxide, greenhouse gases. Better understanding their impact on the climate and the marine environment requires precise measurements of the gas flux. Camera based bubble measurement systems suffer from defocus blur caused by a combination of small depth of field, insufficient lighting and from motion blur due to rapid bubble movement. These adverse conditions are typical for open sea underwater bubble images. As a consequence so called 'bubble boxes' have been built, which use elaborate setups, specialized cameras and high power illumination. A typical value of light power used is 1000W (Leifer et al., 2003). In this paper we propose the compensation of defocus and motion blur in underwater images by using blind deconvolution techniques. The quality of the images can be greatly improved, which will relax requirements on bubble boxes, reduce their energy consumption and widen their usability.

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Section: Cameramentioning

confidence: 99%

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Zelenka

Koch

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The video sequences are taken through a sight-glass of the standard apparatus. The bubble formation is recorded successively on different parts of the tube because different illumination set-ups have to be provided [9]. Different sessions of videos are preserved depending on the operation conditions (heat flux, saturation pressure), on the geometry, the microstructure and macrostructure of the tube, and on the boiling liquid (e.g.…”

Section: Review Of Evaluation Methods For Bubble Formation In Literaturementioning

confidence: 99%

“…Therefore, the background image (Fig. 4, middle) is analysed by succeeding images in situ to represent the illuminated surface of the tube immersed within the saturated liquid under the test conditions in the next step of development of a new evaluation method (for the algorithm see [9,25]). …”

Section: New Methods For the Evaluation Of Video Sequencesmentioning

confidence: 99%

“…[8] for the heat transfer results in detail). New automatically evaluating methods have to be developed because of the large quantity of data within the image sequences [9]. The method is applied to individual bubbles growing on their nucleation sites and they are tracked on their way sliding up the superheated boundary layer around the tube.…”

Section: Introductionmentioning

confidence: 99%

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High speed video recording of bubble formation with pool boiling

Luke

Cheng

2006

International Journal of Thermal Sciences

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“…Snakes [4], [5], [6], contour based models [7], principal component analysis [8], gradient based thresholding methods [9], [10] were utilized by different groups. Even if these methods would give accurate detection results in single frames, they would cause big time delays in videos.…”

Section: Introductionmentioning

confidence: 99%

Automatic Bubble Detection in Cardiac Video Imaging

Parlak¹,

Ademoğlu²,

Egi³

et al. 2011

Polibits

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Abstract-Bubble recognition is a challenging problem in a broad range from mechanics to medicine. These gas-filled structures whose pattern and morphology alter in their surrounding environment would be counted either manually or with computational recognition procedures. In cardiology, user dependent bubble detection and temporal counting in videos require special trainings and experience due to ultra fast movement, inherent noise and video quality. In this study, we propose an efficient recognition routine to increase the objectivity of emboli detection. Firstly, we started to compare five different methods on two synthetic data sets emulating cardiac chamber environment with increasing speckle noise levels. Secondly, real echocardiographic video records were segmented by variational active contours and Left Atria (LA) were extracted. Finally, three successful methods in simulation were applied to LAs in order to reveal candidate bubbles on video frames. Our detection rate of proposed method was 95.7% and the others were 86.2% and 88.3%. We conclude that our approach would be useful in long lasting video processing and would be applied in other disciplines.

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Template-based bubble identification and tracking in image sequences

Cited by 18 publications

References 7 publications

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

Blind Deconvolution on Underwater Images for Gas Bubble Measurement

High speed video recording of bubble formation with pool boiling

Automatic Bubble Detection in Cardiac Video Imaging

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