Flotation Froth Image Segmentation Based on Highlight Correction and Parameter Adaptation

  • Xiu Man Liang
  • Tong TianEmail author
  • Wen Tao Liu
  • Fu Sheng Niu


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.


Froth flotation Image segmentation Watershed algorithm Parameter-adaptive 


Funding Information

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

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Hassanzadeh A, Hassas BV, Kouachi S, Brabcovad Z, Çelik MS (2016) Effect of bubble size and velocity on collision efficiency in chalcopyrite flotation. Colloids & Surfaces A Physicochemical & Engineering Aspects 498:258–267CrossRefGoogle Scholar
  2. 2.
    Aldrich C, Marais C, Shean BJ, Cilliers JJ (2010) Online monitoring and control of froth flotation systems with machine vision: A review. Int J Miner Process 96(1):1–13CrossRefGoogle Scholar
  3. 3.
    Gui W, Yang C, Xu LM, Xie D (2013) Research progress of mineral flotation process monitoring technology based on machine vision. Acta Automat Sin 39(11):1879–1888CrossRefGoogle Scholar
  4. 4.
    Cao B, Xie Y, Gui W, Wei L, Yang C (2013) Integrated prediction model of bauxite concentrate grade based on distributed machine vision. Miner Eng 53:31–38CrossRefGoogle Scholar
  5. 5.
    Zhang J, Tang Z, Liu J, Tan Z, Xu P (2016) Recognition of Flotation Working Conditions through Froth Image Statistical Modeling for Performance Monitoring. Miner Eng 86:116–129CrossRefGoogle Scholar
  6. 6.
    Liang X, Liu W, Niu F, Tian T (2018) Research on Measurement of Volume and Surface Domain of Flotation Bubbles Based on Machine Vision. Acta Opt Sin 38(12):1215009CrossRefGoogle Scholar
  7. 7.
    Morar SH, Harris MC, Bradshaw DJ (2012) The use of machine vision to predict flotation performance. Miner Eng 36-38:31–36CrossRefGoogle Scholar
  8. 8.
    Hosseini MR, Shirazi HHA, Massinaei M, Mehrshad N (2015) Modeling the Relationship between Froth Bubble Size and Flotation Performance Using Image Analysis and Neural Networks. Chem Eng Commun 202(7):911–919CrossRefGoogle Scholar
  9. 9.
    Mehrabi A, Mehrshad N, Massinaei M (2014) Machine vision based monitoring of an industrial flotation cell in an iron flotation plant. Int J Miner Process 133(8):60–66CrossRefGoogle Scholar
  10. 10.
    Jahedsaravani A, Massinaei M, Marhaban MH (2017) An Image Segmentation Algorithm for Measurement of Flotation Froth Bubble Size Distributions. Measurement 111:29–37CrossRefGoogle Scholar
  11. 11.
    Bhondayi C, Moys MH, Tshibwabwa E (2018) Relationship between froth bubble size estimates and flotation performance in a semi-batch lab cell. Miner Process Extr Metall Rev 39(4):284–288CrossRefGoogle Scholar
  12. 12.
    Wang W, Chen L (2015) Flotation Bubble Delineation Based on Harris Corner Detection and Local Gray Value Minima. Minerals 5(2):142–163CrossRefGoogle Scholar
  13. 13.
    Sadrkazemi N (1997) An image processing algorithm for measurement of flotation froth bubble size and shape distributions. Miner Eng 10(10):1075–1083CrossRefGoogle Scholar
  14. 14.
    Yang C, Zhou K, Mou X, Gui W (2009) A method for color and size measurement of flotation foam based on computer vision. Chin J Sci Instrum 30(4):717–721Google Scholar
  15. 15.
    Zhou K, Wang Y, Xu C (2010) Extraction of foam morphological features based on improved FCM and morphology. Journal of Central South University (Science and Technology) 41(3):994–1000Google Scholar
  16. 16.
    Jahedsaravani A, Marhaban MH, Massinaei M, Saripan MI, Mehrshad N (2014) Development of a new algorithm for segmentation of flotation froth images. Miner Metall Process 31(1):66–72Google Scholar

Copyright information

© Society for Mining, Metallurgy & Exploration Inc. 2019

Authors and Affiliations

  1. 1.College of Electrical EngineeringNorth China University of Science and TechnologyTangshanChina
  2. 2.College of Mining EngineeringNorth China University of Science and TechnologyTangshanChina

Personalised recommendations