Ore image segmentation method using U-Net and Res_Unet convolutional networks-Reference-Cited by-同舟云学术

Ore image segmentation method using U-Net and Res_Unet convolutional networks

Published:2020 Issue:16 Volume:10 Page:9396-9406
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Liu Xiaobo¹²³⁴⁵,Zhang Yuwei¹²³⁴⁵,Jing Hongdi¹²³⁴⁵^ORCID,Wang Liancheng¹²³⁴⁵,Zhao Sheng¹²³⁴⁵

Affiliation:

1. Intelligent Mine Research Center

2. Northeastern University

3. Shenyang 110819

4. China

5. National-local Joint Engineering Research Center of High-efficient Exploitation Technology for Refractory Iron Ore Resource

Abstract

Image segmentation has been increasingly used to identify the particle size of crushed ore. How to accurately identify the ore particles in complex a environment is particularly important.

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/RA/C9RA05877J

Reference19 articles.

1. R.-x.Wang , W.-c.Zhang and L.-z.Shao , Research of Ore Particle Size Detection Based on Image Processing , Proceedings of 2017 Chinese Intelligent Systems Conference , 2017 , vol. 460 , pp. 505–514

2. A.Amankwah and C.Aldrich , Automatic ore image segmentation using mean shift and watershed transform , Proceedings of 21st International Conference Radioelektronika 2011 , 2011 , pp. 1–4

3. K.Done and D.-l.Jiang , Automated Estimation of Ore Size Distributions Based on Machine Vision , Lecture Notes in Electrical Engineering , 2014 , vol. 238 , pp. 1125–1131

4. FogBank: a single cell segmentation across multiple cell lines and image modalities

5. In-situ particle segmentation approach based on average background modeling and graph-cut for the monitoring of l -glutamic acid crystallization

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