Skip to main content
SpringerLink
Log in

Ore particle size classification model based on bi-dimensional empirical mode decomposition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The frequency domain information of ore multi-scale image is difficult to distinguish boundary and texture details of ore objects, and hard to apply to particle size detection. Particle size classification model based on Bi-dimensional empirical mode decomposition (BEMD) is proposed, which is applied to particle detection of multi-size scenarios. Different size fraction ore images’ local frequencies are extracted by BEMD reflecting the edge and texture features. The instantaneous frequencies of each high-frequency images are statistic with image multivariate multiscale entropy, and the entropy ranges can be mapped into the particle size distribution. Three different size fraction images are used to verify the accuracy of the proposed method. The experimental results show that the validity and accuracy of our proposed model is better than other methods in multi-scenario ore particle size detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Abhik M, Debashish C, Kousik B, Arpan H (2017) Development of a mass model in estimating weight-wise particle size distribution using digital image processing. Int J Min Sci Technol 27:435–443

    Article  Google Scholar 

  2. Anna Linderhed (2009) Image empirical mode decomposition:a new tool for image processing. Adv Adapt Data Anal 1:265–294

    Article  MathSciNet  Google Scholar 

  3. Artyom M (2015) Grigoryan. Fourier transforms with rotations on circles or ellipses in signal and image processing.IS&T/SPIE Electronic Imaging. Int Soc Opt Photo

  4. Bhattacharya A, Choudhury D, Dey D (2018) Edge-enhanced bi-dimensional empirical mode decomposition-based emotion recognition using fusion of feature set. Soft Comput 22:888–903

    Google Scholar 

  5. Chen Z, Luo S, Xie T et al (2014) A novel infrared small target detection method based on BEMD and local inverse entropy. Infrared Phys Technol 66:114–124

    Article  Google Scholar 

  6. Facco P, Santomaso AC, Barolo M (2017) Artificial vision system for particle size characterization from bulk materials. Chem Eng Sci 164:246–257

    Article  Google Scholar 

  7. Guaragnella C, Manni A, Palumbo F et al. (2010) Bidimensional Empirical Mode Decomposition for Biometric Analysis and Iris Recognition. Comput Intel Measure Syst Appl 30–33

  8. Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc Lond 454(1):903–995

    Article  MathSciNet  Google Scholar 

  9. Huang NE, Wu ZH, Long SR (2009) On Instantaneous Frequency. Adv Adapt Data Anal 1:177–229

    Article  MathSciNet  Google Scholar 

  10. Huang Y, Huang Y et al (2017) Noncontact measurement and detection of instantaneous seismic attributes based on complementary ensemble empirical mode decomposition. Energies 10

    Article  Google Scholar 

  11. Lindeberg T (1996) Scale-space: a framework for handling image structures at multiplescales. Cern Eur Organ Nucl Res-Rep-Cern:27–38

  12. Menga Y, Zhang Z, Yina H, Ma T (2018) Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron 106:34–41

    Article  Google Scholar 

  13. Mondal A, Banerjee P (2018) Hong Tang.A novel feature extraction technique for pulmonary sound analysis based on EMD. Comput Methods Prog Biomed 159:199–209

    Article  Google Scholar 

  14. Nunes JC (2003) Y. Bouaoune,et al.Image analysis by bidimensional empirical mode decomposition. Image Vis Comput 21:1019–1026

    Article  Google Scholar 

  15. Pajares G, de la Cruz JM (2004) A wavelet-based image fusion tutorial. Pattern Recogn 37(9):1855–1872

    Article  Google Scholar 

  16. Qin X, Hu G, Hu K (2018) Image fusion method based on regional feature and improved bidimensional empirical mode decomposition. J Electron Imaging 27

    Article  Google Scholar 

  17. Rajendra Acharya U, Koh JEW, Hagiwara Y (2018) Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Comput Biol Med 94:11–18

    Article  Google Scholar 

  18. Sudarsana B, Jia W, Adamchuka V, Biswas A (2018) Characterizing soil particle sizes using wavelet analysis of microscope images. Comput Electron Agric 148:217–225

    Article  Google Scholar 

  19. Wang J, Shang P et al (2015) EMD based refined composite multiscale entropy analysis of complex signals. Phys A 421:583–593

    Article  Google Scholar 

  20. Yang Y, Ying L, Zhu X (2017) A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition. Acta Oceanol Sin 36:86–94

    Article  Google Scholar 

  21. Yao Q, Lihong Q, Wang Q et al (2018) Bidimensional empirical mode decomposition method for image processing in sensing system. Comput Electr Eng 68:215–224

    Article  Google Scholar 

  22. Zelin Z (2016) Particle overlapping error correction for coal size distribution estimation by image analysis. Int J Miner Process 155:136–139

    Article  Google Scholar 

  23. Zhan Y, Zhang G, Guo Y, Meng H, Zhao X (2017) Particle Size Measurement Based on Image Multivariate Multiscale Entropy, Trustcom/BigDataSE/ICESS. IEEE, 973–977

  24. Zhang S, Liu W (2017) Application of aerial image analysis for assessing particle size segregation in dump leaching. Hydrometallurgy 171:99–105

    Article  Google Scholar 

  25. Zheng J, Hryciw RD (2017) Particulate material fabric characterization by rotational haar wavelet transform. Comput Geotech 88:46–60

    Article  Google Scholar 

Download references

Acknowledgements

We wish to thank Senior Engineer Shu Yu for providing us the Foundation of China Institute of Water Resources and Hydropower Research (GE0145B112017).

Author information

Authors and Affiliations

  1. School of Mechanical Electronic & Information Engineering, China University of Mining &Technology, Beijing, 100083, China

    Yantong Zhan & Guoying Zhang

Authors
  1. Yantong Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yantong Zhan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhan, Y., Zhang, G. Ore particle size classification model based on bi-dimensional empirical mode decomposition. Multimed Tools Appl 79, 4847–4866 (2020). https://doi.org/10.1007/s11042-018-6749-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6749-z

Keywords

Search

Navigation