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Flotation Froth Image Segmentation Based on Highlight Correction and Parameter Adaptation

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Abstract

In order to address the difficulty of accurate segmentation of froth images of different sizes, a method of froth image segmentation based on highlight correction and parameter adaptation is proposed. First, a machine vision system on a single-cell flotation machine is built to collect froth images. Homomorphic filtering is used to improve the uneven brightness and shadow of the images. Fuzzy c-means (FCM) clustering is then utilized to classify similar highlights that belong to the same froth. After Otsu threshold segmentation, a parameter-adaptive morphological operation is used to extract the marker points and edge bands and correct the froth edges in the original image. Finally, the modified image is filtered by morphological reconstruction, and the highlight mark is used as the local minimum point for watershed segmentation. Three sizes of froth images are segmented in comparative experiments. The results show that the proposed method is suitable for the segmentation of froth images of different sizes. The position of the extracted segmentation line is close to reality, with average over-segmentation and under-segmentation rates for froth images of 2.6% and 6.8%, respectively. The froth image segmentation performance is stronger than that of the other methods examined.

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Funding

This study was funded by the National Natural Science Foundation of China (No. 51874135).

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Authors and Affiliations

  1. College of Electrical Engineering, North China University of Science and Technology, Tangshan, 063210, China

    Xiu Man Liang, Tong Tian & Wen Tao Liu

  2. College of Mining Engineering, North China University of Science and Technology, Tangshan, 063210, China

    Fu Sheng Niu

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  1. Xiu Man Liang
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  2. Tong Tian
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  3. Wen Tao Liu
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Correspondence to Tong Tian.

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Liang, X.M., Tian, T., Liu, W.T. et al. Flotation Froth Image Segmentation Based on Highlight Correction and Parameter Adaptation. Mining, Metallurgy & Exploration 37, 467–474 (2020). https://doi.org/10.1007/s42461-019-00137-0

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  • DOI: https://doi.org/10.1007/s42461-019-00137-0

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