Region-Based Concave Point Matching for Separating Adhering Objects in Industrial X-Ray of Tungsten Ores
Source abstract
Abstract
Efficient and rational utilization of mineral resources significantly impacts economic and technological development. Image segmentation is a pivotal process in ore sorting, as its results directly affect the accuracy of mineral classification. Traditional segmentation methods often fail to meet the requirements for noise suppression, segmentation precision, and robustness in ore sorting. To address these issues, we propose an ore image segmentation method based on concavity matching via region retrieval, which comprises a contour approximation module, a concavity matching module, and a segmentation detection module. It introduces the concepts of single-contour, multi-contour, and segmentation regions in ore images, offering tailored segmentation approaches for varying adhesion forms and quantities. A significant contribution of this study lies in the contour approximation module, which simplifies the edge information of ore images via curve fitting, effectively removing the influence of edge noise points. The concavity matching module restricts candidate areas for matching concavity points through the construction of search regions, significantly improving matching accuracy. Finally, paired concavity points are connected to completing the segmentation process. Experimental comparisons using X-ray images of tungsten ores demonstrate that the proposed method can effectively suppress noise-induced concavity interference, achieving a noise reduction efficiency of 94.77% and a concavity region search accuracy of 93.60%, thus meeting the precision requirements for segmenting X-ray ore images. Given its high efficiency and accuracy, industrial sectors involved in mineral processing are recommended to incorporate this segmentation method into intelligent ore sorting equipment upgrading and renovation projects, enhancing the overall efficiency of mineral resource sorting and promoting the sustainable development of the mineral industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mineral Processing and Grinding · Advanced X-ray and CT Imaging · Minerals Flotation and Separation Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Jiangxi Province Engineering Research Center of New Energy Technology and Equipment, East China University of Technology, Nanchang 330013, China
National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, Nanchang 330013, China
Funding
National Natural Science Foundation of China · Key Research and Development Project of Jiangxi Province · Jiangxi Provincial Natural Science Foundation
References (24)
Show source-provided references
1. Jung, D., and Choi, Y. (2021). Systematic review of machine learning applications in mining: Exploration, exploitation, and reclamation. Minerals, 11.10.3390/min11020148 ↗
2. Machine learning applications in minerals processing: A review10.1016/j.mineng.2018.12.004 ↗
3. Robben, C., and Wotruba, H. (2019). Sensor-based ore sorting technology in mining—Past, present and future. Minerals, 9.10.3390/min9090523 ↗
4. Image segmentation of adhesive ores based on MSBA-Unet and convex-hull defect detection10.1016/j.engappai.2023.106185 ↗
5. Deep learning in image segmentation for mineral production: A review10.1016/j.cageo.2023.105455 ↗
6. Efficient segmentation with texture in ore images based on box-supervised approach10.1016/j.engappai.2023.107490 ↗
7. Ore image segmentation method of conveyor belt based on U-Net and Res_UNet models
8. Recent progress in experimental investigation of neutron irradiation response of tungsten10.1016/j.jnucmat.2022.153856 ↗
9. Nondestructive determination of isotopic abundance using multi-energy nuclear resonance fluorescence driven by laser Compton scattering source10.1063/5.0197076 ↗
10. Li, Y., and Duan, Y. (2018, January 15–18). A method of ore image segmentation based on deep learning. Proceedings of the Intelligent Computing Methodologies: 14th International Conference, ICIC 2018, Wuhan, China. Proceedings, Part III 14.
11. Ore image segmentation method based on u-net and watershed. Computers
12. Ore image segmentation by learning image and shape features10.1016/j.patrec.2008.12.015 ↗
13. DDR-Unet: A High Accuracy and Efficient Ore Image Segmentation Method
14. Segmentation and counting algorithm for touching hybrid rice grains10.1016/j.compag.2019.04.030 ↗
15. A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis10.1016/j.compag.2014.09.015 ↗
16. Automatic segmentation for cell images based on bottleneck detection and ellipse fitting10.1016/j.neucom.2015.08.006 ↗
17. Automated segmentation of overlapped nuclei using concave point detection and segment grouping10.1016/j.patcog.2017.06.021 ↗
18. Yao, Y., Wu, W., Yang, T., Liu, T., Chen, W., Chen, C., Li, R., Zhou, T., Sun, C., and Zhou, Y. (2017). Head rice rate measurement based on concave point matching. Sci. Rep., 7.10.1038/srep41353 ↗
19. Splitting ore from X-ray image based on improved robust concave-point algorithm10.7717/peerj-cs.1263 ↗
20. He, L., Wang, S., and Guo, Y. (2022). Detection of Pits by Conjugate Lines: An Algorithm for Segmentation of Overlapping and Adhesion Targets in DE-XRT Sorting Images of Coal and Gangue. Appl. Sci., 12.10.3390/app12199850 ↗
21. Application of concave point matching algorithm in segmenting overlapping coal particles in X-ray images10.1016/j.mineng.2021.107096 ↗
22. An iterative procedure for the polygonal approximation of plane curves10.1016/S0146-664X(72)80017-0 ↗
23. Topological structural analysis of digitized binary images by border following10.1016/0734-189X(85)90016-7 ↗
24. Algorithm for computer control of a digital plotter