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

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  • Dong-heng Xie
  • Ming Lu
  • Yong-fang Xie
  • Duan Liu
  • Xiong Li

Abstract

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.

Suggested Citation

  • Dong-heng Xie & Ming Lu & Yong-fang Xie & Duan Liu & Xiong Li, 2019. "A fast threshold segmentation method for froth image base on the pixel distribution characteristic," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-18, January.
    • Handle: RePEc:plo:pone00:0210411
    DOI: 10.1371/journal.pone.0210411
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    References listed on IDEAS

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    1. Zhiyong Dong & Ranfeng Wang & Minqiang Fan & Xiang Fu, 2017. "Switching and optimizing control for coal flotation process based on a hybrid model," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-20, October.
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    Cited by:

    1. Ke Fang, 2022. "Threshold segmentation of PCB defect image grid based on finite difference dispersion for providing accuracy in the IoT based data of smart cities," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 121-131, March.
    2. Sadia Basar & Mushtaq Ali & Gilberto Ochoa-Ruiz & Mahdi Zareei & Abdul Waheed & Awais Adnan, 2020. "Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    3. Pinto, Erveton P. & Pires, Marcelo A. & Matos, Robert S. & Zamora, Robert R.M. & Menezes, Rodrigo P. & Araújo, Raquel S. & de Souza, Tiago M., 2021. "Lacunarity exponent and Moran index: A complementary methodology to analyze AFM images and its application to chitosan films," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).

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