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Volume 18 Issue 4
Aug.  2011
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Guo-ying Zhang, Guan-zhou Liu,  and Hong Zhu, Segmentation algorithm of complex ore images based on templates transformation and reconstruction, Int. J. Miner. Metall. Mater., 18(2011), No. 4, pp. 385-389. https://doi.org/10.1007/s12613-011-0451-8
Cite this article as:
Guo-ying Zhang, Guan-zhou Liu,  and Hong Zhu, Segmentation algorithm of complex ore images based on templates transformation and reconstruction, Int. J. Miner. Metall. Mater., 18(2011), No. 4, pp. 385-389. https://doi.org/10.1007/s12613-011-0451-8
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Segmentation algorithm of complex ore images based on templates transformation and reconstruction

  • 通讯作者:

    Guo-ying Zhang    E-mail: zhangguoying1101@163.com

  • Lots of noises and heterogeneous objects with various sizes coexist in a complex image, such as an ore image; the classical image thresholding method cannot effectively distinguish between ores. To segment ore objects with various sizes simultaneously, two adaptive windows in the image were chosen for each pixel; the gray value of windows was calculated by Otsu’s threshold method. To extract the object skeleton, the definition principle of distance transformation templates was proposed. The ores linked together in a binary image were separated by distance transformation and gray reconstruction. The seed region of each object was picked up from the local maximum gray region of the reconstruction image. Starting from these seed regions, the watershed method was used to segment ore object effectively. The proposed algorithm marks and segments most objects from complex images precisely.
  • Segmentation algorithm of complex ore images based on templates transformation and reconstruction

    + Author Affiliations
    • Lots of noises and heterogeneous objects with various sizes coexist in a complex image, such as an ore image; the classical image thresholding method cannot effectively distinguish between ores. To segment ore objects with various sizes simultaneously, two adaptive windows in the image were chosen for each pixel; the gray value of windows was calculated by Otsu’s threshold method. To extract the object skeleton, the definition principle of distance transformation templates was proposed. The ores linked together in a binary image were separated by distance transformation and gray reconstruction. The seed region of each object was picked up from the local maximum gray region of the reconstruction image. Starting from these seed regions, the watershed method was used to segment ore object effectively. The proposed algorithm marks and segments most objects from complex images precisely.
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