DE-XRT coal preparation image overlapping and adhesion particle segmentation method

• Autorzy: He Lei , Wang Shuang , Guo Yongcun

Identyfikatory

DOI

10.37190/ppmp/155840

• Języki publikacji: EN

Abstrakty

EN

Accurate segmentation of dual-energy X-ray transmission (DE-XRT) coal and gangue image regions are a prerequisite for feature extraction, identification, localization, and separation. A watershed algorithm based on multi-grayscale threshold segmentation (MGTS) is proposed to mark the foreground for the adhesion and overlap of coal and gangue. The grayscale images of foreground objects are segmented using multiple grayscale thresholds, and the number of connected domains is recorded each time. As the gray threshold value decreases, overlapping and adhering objects are gradually separated. The binary image segmented at the grayscale threshold with the most significant number of connected domains is used as a marker region. This marker region is used as the seed point of the watershed algorithm to find the dividing line. The experimental results show that the segmentation accuracy is 91.35%, and the segmentation accuracy of overlapping adhesions of 2, 3, and 4 targets is higher than 90%.

Słowa kluczowe

EN

overlap; adhesion; image segmentation; watershed algorithm; DE-XRT; coal; gangue

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2022
• Tom: Vol. 58, iss. 6
• Strony: art. no. 155840
• Opis fizyczny: Bibliogr. 21 poz., rys.,wykr.

Twórcy

autor

He Lei

• School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

autor

Wang Shuang

• School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China
• [e-mail]

autor

Guo Yongcun

• School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).