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Robust Approach to Detection of Bubbles Based on Images Analysis

Huong T. Nguyen, Long T. Nguyen, Aliona I. Dreglea

Abstract



Bubbles detection is important in various applications in areas including medicine, process control, geochemistry. The application of computer vision methods enables robust bubbles detection and classification even in complex image registration environments. Complex image background is one of the key issues we studied. Proposed method uses image
segmentation based on graph cuts algorithm. Haar wavelet transform algorithm is applied on feature selection stage. The efficiency has been optimized for a continuous update of a
list of voting points based on the accumulator size and position of bubbles. The efficency of proposed approach is demonstrated on the real dataset.

Keywords


bubbles detection, machine learning, image processing, image segmentation, graph cuts algorithm, Haar wavelet transform, feature extraction.

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