An image segmentation algorithm for measurement of flotation froth bubble size distributions

Jahedsaravani, A.; Massinaei, M.; Marhaban, Mohammad Hamiruce

doi:10.1016/j.measurement.2017.07.023

Cited by 53 publications

(11 citation statements)

References 12 publications

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“…Tian et al [15] applied the particle swarm optimization algorithm and fuzzy C means algorithm to extract foam foreground markers showing a slight increase in accuracy. Jahedsaravani et al [16] designed a neural network to train a sub-image classifier to obtain key parameters to deal with different sizes of foam images and then divided them by a watershed algorithm. However, for adaptive bubbles with a large amount of noise and bright edges, the adaptive threshold method cannot reduce the interference of nonbubble highlights and cannot obtain good segmentation results.…”

Section: Introductionmentioning

confidence: 99%

Bubble Image Segmentation Based on a Novel Watershed Algorithm With an Optimized Mark and Edge Constraint

Peng

Liu

Gui

et al. 2022

IEEE Trans. Instrum. Meas.

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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 paper, 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.

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Section: Introductionmentioning

confidence: 99%

Bubble Image Segmentation Based on a Novel Watershed Algorithm With an Optimized Mark and Edge Constraint

Peng

Liu

Gui

et al. 2022

IEEE Trans. Instrum. Meas.

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“…According to the four types of froth listed in the literature [3], four classes of froth images are selected, and a total of 12 images are used in the experiment to test the performance of the proposed method and Otsu method. The size of each image is 800×600.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Froth image contains a large number of closely packed froths with weak edges affected the quality of image segmentation. A wide range of froth size and uneven illumination are other difficulties in accurate segmentation of froth images [3]. To overcome these problems, scholars developed algorithms and methods in this field.…”

Section: Introductionmentioning

confidence: 99%

A fast threshold segmentation method for froth image base on the pixel distribution characteristic

Xie

et al. 2019

PLoS ONE

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With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time.

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“…ϕ(m, n) = A max − A min A max + A min (14) where, A max and A min , respectively, are the maximum and the minimum of grey level values of a 3 × 3 window centered by ϕ(m, n). Table 1 gives the quantitative indicators of the three different types of bubble images.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In recent years, there are still many researchers who made different studies for this kind of research topic [8][9][10][11][12][13][14][15][16][17]. Their systems were different based on different flotation material types and environments, and some of them obtained the good quality images under the corresponding conditions and materials; even so, in the images, there are mostly similar problems of uneven lightings shadows, texture loss, etc.…”

Section: Introductionmentioning

confidence: 99%

Froth Image Acquisition and Enhancement on Optical Correction and Retinex Compensation

et al. 2018

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To well monitor and optimize the flotation production, a computer vision and image analysis system is used. In such a system, the first important step is to acquire the froth surface images in high quality. Froth imaging quality is hard to control, and the industrial field noise, froth 3D properties, complex textures, and mixed colors can also cause the flotation image to be difficult to segment and process. To acquire high quality images, a new system for image acquisition of the lead flotation is studied. The system constructs the free-form surface lens based on the non-imaging optics theory, which can improve the optical efficiency of the lens and the uniformity of light sources, and can reduce flare effects. For the compensation, an improved MSR (Multi-Scale Retinex) adaptive image algorithm is proposed to increase the brightness and intensity contrast for small bubbles, and to enhance texture details and froth weak edges by analyzing the Retinex output characteristics of the shaded area and improving the gain function. Under the condition of the optimal parameters, the image acquisition system can obtain uniform illumination and reduce different noises. Experiments show that the new froth image acquisition system increases Signal/Noise by 14%, contrast by 21%, and image segmentation accuracy by 26% in an image.

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An image segmentation algorithm for measurement of flotation froth bubble size distributions

Cited by 53 publications

References 12 publications

Bubble Image Segmentation Based on a Novel Watershed Algorithm With an Optimized Mark and Edge Constraint

Bubble Image Segmentation Based on a Novel Watershed Algorithm With an Optimized Mark and Edge Constraint

A fast threshold segmentation method for froth image base on the pixel distribution characteristic

Froth Image Acquisition and Enhancement on Optical Correction and Retinex Compensation

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