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University of Monastir, Monastir, Tunisia

**DOI: **10.18287/2412-6179-CO-605

**Страницы:** 363-374.

**Язык статьи:** English

Аннотация:

Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classify bubbles in the image into a solitary bubble and overlapping bubbles. The purpose of the second step is overlapping bubbles segmentation. This step is performed in two subsequent steps: at first, we classify bubble clusters into touching and communicating bubbles. Then, the boundaries of communicating bubbles are split into segments based on concave point extraction. The last step in our algorithm addresses segments grouping to merge all contour segments that belong to the same bubble and circle/ellipse fitting to reconstruct the missing part of each bubble. An application of the proposed technique to computer generated and high-speed real air bubble images is used to assess our algorithm. The developed method provides an accurate and computationally effective way for overlapping bubbles segmentation. The accuracy rate of well segmented bubbles we achieved is greater than 90 % in all cases. Moreover, a computation time equal to 12 seconds for a typical image (1 Mpx, 150 overlapping bubbles) is reached.

Ключевые слова:

bubble images; highly overlapping bubbles; bubble recognition; image segmentation; digital image processing.

Цитирование:

Bettaieb A, Filali N, Filali T, Ben Aissia H. An efficient algorithm for overlapping bubbles segmentation. Computer Optics 2020; 44(3): 363-374. DOI: 10.18287/2412-6179-CO-605.

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