Abstract
Bubble size contains important indication information that is closely related to flotation production conditions and process indicators. However, bubble images often have low contrast, noise, and many other shortcomings, making foam segmentation a difficult problem that the existing segmentation methods cannot solve. In this article, an improved watershed algorithm based on optimal labeling and edge constraints is proposed. Three algorithms are designed to obtain different initial tags, and then the extracted content of different tags is fused to obtain the combined foreground tag. To reduce the offset of the segmentation line, the edge operator is applied to extract the bubble boundary, and the boundary priori condition is used as a constraint to correct the segmentation line. Finally, the optimal segmentation line is obtained by fusing foreground markers and external constraints. Industrial experiments show that this method is effective and has a higher accuracy than the other methods. The average value and variance of rand index (RI) are 92.88% and 0.69, respectively.
Highlights
In the visual characteristics of the foam surface, the shape of the foam and the size distribution of bubbles are the most observed foam morphology characteristics
(2) To reduce the influence of bright edge and white point noise on the segmentation line migration, we use the Gauss Laplace operator and morphological operator to extract the edge of the bubble image and use the edge line to reconstruct the gradient map to form a constraint on the watershed algorithm
Image segmentation based on markers often causes the offset of the segmentation line in the foam image
Summary
In the visual characteristics of the foam surface, the shape of the foam and the size distribution of bubbles are the most observed foam morphology characteristics. Morphological extraction will extract some foreground markers as bright edges and cannot achieve good segmentation results on images containing small bubbles. Due to the above problems, this paper considers the following points to improve the algorithm: (1) To extract more promising and robust foreground markers in flotation froth images of different times, we choose three algorithms (the fuzzy C-means algorithm, the morphological reconstruction method, and the adaptive threshold method.) to extract and fuse the foam foreground markers. (2) To reduce the influence of bright edge and white point noise on the segmentation line migration, we use the Gauss Laplace operator and morphological operator to extract the edge of the bubble image and use the edge line to reconstruct the gradient map to form a constraint on the watershed algorithm.
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