Skip to main content

A Target Extraction Algorithm Based on Polarization Image Attention Mechanism

  • Conference paper
  • First Online:
Advances in Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1586))

Included in the following conference series:

  • 843 Accesses

Abstract

Polarization images can highlight the reflection attribute of the target and enhance the display effect of the target boundary on the images. More comprehensive target attributes can be obtained from images taken from multiple polarization angles. However, the number of polarization images is limited at present, and it is difficult to carry out effective research on the premise of small samples. Therefore, based on the polarization images, we give full play to the advantages of polarization images, improve the traditional neural network, and introduce the visual attention model to focus on the salient region. Based on the structural similarity between hyperspectral images and polarization images, twin convolution neural network is constructed to carry out the research of transfer learning in order to learn the network parameters in the case of small samples, and finally realize the target extraction.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hossain, I., Samsuzzaman, M., Hoque, A., Baharuddin, M.H., Binti, N.: Polarization insensitive broadband zero indexed nano-meta absorber for optical region applications. Comput. Mater. Continua 71(1), 993–1009 (2022)

    Article  Google Scholar 

  2. Tian, Q., Cao, M., Chen, S., Yin, H.: Structure-exploiting discriminative ordinal multi-output re-gression. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 266–280 (2021)

    Google Scholar 

  3. Sarkar, M., Bello, D.S.S.S.S., Hoof, C.V., Theuwissen, A.: Integrated polarization analyzing CMOS image sensor for material classification. IEEE Sens. J. 11(8), 1692–1703 (2010)

    Google Scholar 

  4. Gruev, V., Spiegel, J.V.D., Engheta, N.: Dual-tier thin film polymer polarization imaging sensor. Opt. Express 18(18), 19292–19303 (2010)

    Article  Google Scholar 

  5. Huang, K.C., Chang, C.L., Wu, W.H.: Novel image polarization method for measurement of lens decentration. IEEE Trans. Instrum. Meas. 60(5), 1845–1853 (2011)

    Article  Google Scholar 

  6. Yang, F.B., Li, W.W., Lin, S.Z., Wang, F.Y.: Study on fusion of infrared polarization and intensity images. Infrared Technol. 33(5), 262–266 (2011)

    Google Scholar 

  7. Spiegel, J.V.D., Wu, X., Zhang, M., Engheta, N.: Polarization image sensors: learning from biology to make the invisible visible. In: 2012 IEEE International Conference on Electron De-vices and Solid State Circuit (EDSSC), pp. 1–3 (2012)

    Google Scholar 

  8. Zhang, S., Yuan, Y., Su, L., Hu, L., Liu, H.: Polarization image fusion algorithm based on improved PCNN. In: 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, International Society for Optics and Photonics, vol. 9045, p. 90450B (2013)

    Google Scholar 

  9. Zhang, D.X., Wang, H.H., Xue, F.: Fusion of polarization image based on curvelet transform. Appl. Mech. Mater. 536, 111–114 (2014)

    Google Scholar 

  10. Haining, Y., Liangmei, H., Zhiguo, F.: Fusion method for polarization images based on anal- ysis of features. J. Appl. Opt. 36(2), 220–226 (2015)

    Google Scholar 

  11. Zhang, D., Yuan, B., Zhang, J.: Research on fusion algorithm of polarization image in tetrolet domain. In: Sixth International Conference on Electronics and Information Engineering, vol. 9794, p. 97941Q. International Society for Optics and Photonics (2015)

    Google Scholar 

  12. Zhang, L., Yang, F.B., Ji, L., Yuan, H., Dong, A.: A categorization method of infrared po- larization and intensity image fusion algorithm based on the transfer ability of difference features. Infrared Phys. Technol. 79, 91–100 (2016)

    Article  Google Scholar 

  13. Ming, Y., Jiyong, P., Yuanyuan, W., Puhong, D.: Image fusion algorithm based on nonsubsampled dual-tree complex contourlet transform and compressive sensing pulse coupled neural network. J. Comput. Aided Des. Comput. Graph. 28, 411–419 (2016)

    Google Scholar 

  14. Li, X., Huang, Q.: Target detection for infrared polarization image in the background of desert. In: 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), pp. 1147–1151 (2017)

    Google Scholar 

  15. Calisti, M., Carbonara, G., Laschi, C.: A rotating polarizing filter approach for image enhancement. OCEANS 2017-Aberdeen, pp. 1–4 (2017)

    Google Scholar 

  16. Zhu, P., Ding, L., Ma, X., Huang, Z.: Fusion of infrared polarization and intensity images based on improved toggle operator. Opt. Laser Technol. 98, 139–151 (2018)

    Article  Google Scholar 

  17. Zhang, J.H., Zhang, Y., Shi, Z.: Long-wave infrared polarization feature extraction and image fusion based on the orthogonality difference method. J. Electron. Imaging 27(2), 23021 (2018)

    Article  Google Scholar 

  18. Wang, X., Sun, J., Xu, Z., Chang, J.: Polarization image fusion algorithm based on global in-formation correction. In: Proceedings of the 2nd International Conference on Image and Graphics Processing, pp. 98–104 (2019)

    Google Scholar 

  19. Zhang, J., Zhou, H., Wei, S., Tan, W.: Infrared polarization image fusion via multi-scale sparse representation and pulse coupled neural network. In: International Society for Optics and Photonics, vol. 11338, p. 113382A. International Society for Optics and Photonics (2019)

    Google Scholar 

  20. Xie, F., Chen, J.: A new polarized image fusion algorithm based on two-scale guided filtering. In: 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1150–1155 (2020)

    Google Scholar 

  21. Jiang, Z., Han, Y., Ye, F., Ren, S., Zhai, H., Hu, Z.: A visible polarization image fusion algo- rithm based on NSST transform. In: International Society for Optics and Photonics, vol. 11567, p. 115671V. International Society for Optics and Photonics (2020)

    Google Scholar 

  22. Wang, S., Meng, J., Zhou, Y., Hu, Q., Wang, Z., Lyu, J.: Polarization image fusion algorithm Using NSCT and CNN. J. Russ. Laser Res. 42(4), 443–452 (2021)

    Article  Google Scholar 

  23. Qiu, S., Luo, J., Yang, S., Zhang, M., Zhang, W.: A moving target extraction algorithm based on the fusion of infrared and visible images. Infrared Phys. Technol. 98, 285–291 (2019)

    Article  Google Scholar 

  24. Shujaat, M., Aslam, N., Noreen, I., Ehsan, M.K., Qureshi, M.: Intelligent and integrated framework for exudate detection in retinal fundus images. Intel. Autom. Soft Comput. 30(2), 663–672 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Fengchang Fei or Cong Nie .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, J., Fei, F., Wang, Z., Nie, C. (2022). A Target Extraction Algorithm Based on Polarization Image Attention Mechanism. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1586. Springer, Cham. https://doi.org/10.1007/978-3-031-06767-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06767-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06766-2

  • Online ISBN: 978-3-031-06767-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics