ORIGINAL PAPER
Image segmentation method of mine pass soil and ore based on the fusion of the confidence edge detection algorithm and mean shift algorithm
1
BGRIMM Technology Group
2
University of Science and Technology Beijing
Submission date: 2021-07-28
Final revision date: 2021-10-07
Acceptance date: 2021-11-08
Publication date: 2021-12-22
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2021;37(4):133-152
KEYWORDS
TOPICS
Rational and efficient development and utilization of mineral resourcesRational use of mineral raw materials
ABSTRACT
In the execution of edge detection algorithms and clustering algorithms to segment image containing ore and soil, ore images with very similar textural features cannot be segmented effectively when the two algorithms are used alone. This paper proposes a novel image segmentation method based on the fusion of a confidence edge detection algorithm and a mean shift algorithm, which integrates image color, texture and spatial features. On the basis of the initial segmentation results obtained by the mean shift segmentation algorithm, the edge information of the image is extracted by using the edge detection algorithm based on the confidence degree, and the edge detection results are applied to the initial segmentation region results to optimize and merge the ore or pile belonging to the same region. The experimental results show that this method can successfully overcome the shortcomings of the respective algorithm and has a better segmentation results for the ore, which effectively solves the problem of over segmentation.
ACKNOWLEDGEMENTS
This work was jointly supported by the National Key Research and Development Program of China (No. 2018YFC0604403) and the Scientific Research Fund of BGRIMM Technology Group (No. 02-2036-WG).
METADATA IN OTHER LANGUAGES:
Polish
Metoda segmentacji obrazu gleby i rudy w oparciu o połączenie algorytmu wykrywania krawędzi ufności i algorytmu zmiany średniej
wykrywanie krawędzi, ufność, algorytm zmiany średniej, segmentacja obrazu
W procesie algorytmu wykrywania krawędzi ufności i algorytmu grupowania do segmentacji obrazu zawierającego rudę i glebę, obraz rudy o bardzo podobnych cechach tekstury nie może być skutecznie segmentowany, gdy oba algorytmy są używane osobno. W pracy zaproponowano nowatorską metodę segmentacji obrazu opartą na połączeniu algorytmu wykrywania krawędzi ufności i algorytmu zmiany średniej, który integruje kolor, teksturę i cechy przestrzenne obrazu. Na podstawie wstępnych wyników segmentacji uzyskanych przez algorytm segmentacji zmiany średniej informacja o krawędziach oryginalnego obrazu jest wyodrębniana za pomocą algorytmu wykrywania krawędzi opartego na stopniu ufności, a otrzymane wyniki są stosowane do początkowych wyników segmentacji obszaru w celu optymalizacji i scalenia rudy lub gleby należących do tego samego obszaru. Wyniki eksperymentalne pokazują, że metoda ta może skutecznie przezwyciężyć wady odpowiedniego algorytmu i daje lepsze wyniki segmentacji dla rudy, co dobrze rozwiązuje problem nadmiernej segmentacji.
REFERENCES (16)
1.
Boyuan et al. 2021 – Boyuan, M., Shufang, J., Dou, Y., Haokai, S. and Xiaojuan, B. 2021. Image segmentation metric and its application in the analysis of microscopic image. Chinese Journal of Engineering 43, pp. 137–149. DOI: 10.13374/j.issn2095-9389.2020.05.28.002.
2.
Canny, J. 1986. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, pp. 679–698.
3.
Cheng, Y. 1995. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, pp. 790–799.
4.
Comaniciu, D. and Meer, P. 1997. Robust analysis of feature spaces: color image segmentation. Conference on Computer Vision and Pattern Recognition.
5.
Comaniciu, D. and Meer, P. 1999. Mean shift analysis and applications. Proceedings of the seventh IEEE international conference on computer vision. IEEE, pp. 1197–1203.
6.
Comaniciu, D. and Meer, P. 2002. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, pp. 603–619. DOI: 10.1109/34.1000236.
7.
Georgescu ae al. 2003 – Georgescu, B., Shimshoni, I. and Meer, P. 2003. Mean shift based clustering in high dimensions: A texture classification example. Computer Vision, IEEE International Conference on, 2003. IEEE Computer Society, pp. 456–456.
8.
Lin et al. 2005 – Lin, K., Wu, J. and Xu, L. 2005. A survey on color image segmentation techniques. Journal of Image and Graphics 10, pp. 1–10. DOI: 10.1007/s41095-020-0177-5.
9.
Liu et al. 2020 – Liu, X., Zhang, Y., Jing, H., Wang, L. and Zhao, S. 2020. Ore image segmentation method using U-Net and Res_Unet convolutional networks. RSC Advances 10, pp. 9396–9406. DOI: 10.1039/C9RA05877J.
10.
Meer, P. and Georgescu, B. 2001. Edge Detection with Embedded Confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(12), pp. 1351–1365.
11.
Qin, C. and Liu, Q. 2015. Image segmentation of pellets based on improved watershed algorithm. Journal of Wuhan University of ence and Technology, pp. 56–59.
12.
Rui, Z. and Jintao, L. 2020. A Survey on Algorithm Research of Scene Parsing Based on Deep Learning. Journal of Computer Research and Development 57(4), pp. 859–875. DOI: 10.7544/issn1000-1239.2020.20190513.
14.
Xiang-Ru et al. 2005 – Xiang-Ru, L.I., Fu-Chao, W.U. and Zhan-Yi, H.U. 2005. Convergence of a mean shift algorithm. Journal of Software 16(3), pp. 365–374.
15.
Xu et al. 2019 – Xu, W., Zhang, G., Jiang, Y. and Chen, L. 2019. Ore segmentation method by Bi-window OTSU based on binomial distribution. Nonferrous Metals (Mining Section) 71, pp. 101–107. DOI: 10.3969/j.issn.1671-4172.2019.03.021.
16.
Ying et al. 2005 – Ying, L., Song, H.N. and Yi, S.U. 2005. An Approach to Edge Detection Based on Confidence. Computer Simulation 22, pp. 195–196.
| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
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