基于改进GMS特征匹配算法的浮选泡沫移动速度特征提取

doi:10.11896/jsjkx.211000064

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000064-6.doi: 10.11896/jsjkx.211000064

• Image Processing & Multimedia Technology • Previous Articles Next Articles

Feature Extraction of Flotation Foam Moving Speed Based on Improved GMS Feature Matching Algorithm

LIU Hui-zhong^1,2, YU Hua-fu¹, PENG Zhi-long¹

1 School of Mechanical and Electrical Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China
2 Jiangxi Mining and Metallurgy Electromechanical Engineering Technology Research Center,Ganzhou,Jiangxi 341000,China

Online:2022-11-10 Published:2022-11-21
About author:LIU Hui-zhong,born in 1969,Ph.D,professor,Ph.D supervisor.His main research interests include mining and metallurgy equipment and intelligence.
Supported by:
Jiangxi Province “Thousand Talents Plan” Introduction of Innovative High-level Talents Project(JXSQ2018101046).

Abstract

Abstract: In the process of mineral flotation,there is a great correlation between the moving speed of flotation foam and the control of flotation process.If the dynamic characteristics such as the moving speed of flotation foam can be accurately obtained in real time,it can provide a basis for the optimization and adjustment of control parameters such as liquid level,charging amount and aeration amount in the flotation process.In order to obtain the moving speed of flotation foam effectively,a feature extraction method based on improved GMS feature matching algorithm is proposed in this paper.Firstly,the ORB algorithm is used to extract and describe the foam feature points,and then the GMS feature matching algorithm is used to complete the fast matching of feature point pairs.On the basis of the above,the RANSAC algorithm is used to eliminate the false matching points in the feature matching results.Finally,the foam moving speed is obtained by calculating the displacement of the foam feature points.The application test of the collected industrial image data shows that the proposed algorithm not only solves the problem that there are many false matching points in the flotation foam image feature extraction of traditional algorithm,but also effectively improves the efficiency and stability of flotation foam feature extraction.

Key words: Flotation foam image, ORB algorithm, GMS feature matching, RANSAC algorithm, Foam movement velocity extraction

CLC Number:

TP391

LIU Hui-zhong, YU Hua-fu, PENG Zhi-long. Feature Extraction of Flotation Foam Moving Speed Based on Improved GMS Feature Matching Algorithm[J].Computer Science, 2022, 49(11A): 211000064-6.

References

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