A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Zhang, Hu; Tang, Zhaohui; Xie, Yongfang; Gao, Xiaoliang; Chen, Qing

doi:10.1016/j.measurement.2019.02.005

Cited by 63 publications

(16 citation statements)

References 32 publications

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“…The method in this paper effectively overcomes the above problem, and it makes the deviation of the calculated maturity of the cotton sample smaller and more accurate, with an average error of 0.023. In addition, we also select several widely used image segmentation algorithms to compare with our method, including super-pixel segmentation method [27,28], watershed method [29,30], and automatic contour method [31,32]. The corresponding experimental results are shown in Figure 16.…”

Section: Influence Test and Analysis Of Multi-scale Edge Detection And Fusion Of Image Pyramidmentioning

confidence: 99%

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

He¹,

Wang²

2021

Information

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Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

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Section: Influence Test and Analysis Of Multi-scale Edge Detection And Fusion Of Image Pyramidmentioning

confidence: 99%

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

He¹,

Wang²

2021

Information

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“…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. To solve the problem of [16], Zhang et al [17] proposed a watershed algorithm based on optimal labeling. In this algorithm, the data are fused with three kinds of labeled areas, and the overlapping markers are combined and optimized.…”

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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“…Froth image analysis has advanced considerably, since computer vision systems became industrially established in the 1990s. This includes the measurement of bubble size distributions as a means to characterize froths [11][12][13], the measurement of froth colour, froth stability, and froth velocity patterns that are used on the control of flotation plants. These methods do not require information additional to the images and are well-supported by commercial software.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

Aldrich

Liu

2021

Minerals

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Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.

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A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Cited by 63 publications

References 32 publications

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

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

Monitoring of Flotation Systems by Use of Multivariate Froth Image Analysis

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