Skip to main content
SpringerLink
Log in

Infrared Image Extraction Algorithm Based on Adaptive Growth Immune Field

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

In criminal investigation, there are hidden traces that many people can’t find, so infrared image is becoming an effective means to obtain these scene traces. The extraction algorithm with growth immune field can extract the target of infrared image relatively effectively, but it is lack of efficiency and reliability in complex environment. Here we propose a new target extraction algorithm with adaptive growth immune field, combining the image information of region and edge gradient. The region of the target in complex environment is obtained by K-means clustering algorithm and the source seed points are selected from the region. The regional characteristics around the seed points as the criteria for growth and the image gradient information is applied as the condition of the adaptive growth immune field. This algorithm improves the accuracy of target extraction in complex environment while preventing overgrowth. We compare the algorithm with the original algorithm and other algorithms and we find that the new algorithm combining edge gradient information can reduce the probability of over growth and ensure the integrity of target extraction under complex background.

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

Similar content being viewed by others

References

  1. Fernandes HC, Maldague X, Batista MA et al (2011) Suspicious event recognition using infrared imagery. In: IEEE international conference on systems, man, and cybernetics, IEEE, pp 2186–2191

  2. Hurney P, Waldron P, Morgan F et al (2014) Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors. IET Intel Transp Syst 9(1):75–85

    Article  Google Scholar 

  3. Pooja KM, Rajesh R, Kumari J (2015) On the segmentation of pedestrians in infrared images. In: IEEE international conference on research in computational intelligence and communication networks, IEEE, pp 44–49

  4. Kaur P, Singh H, Kumar V (2017) Salient infrared target and visible image fusion based on morphological segmentation. In: 2017 4th international conference on signal processing, computing and control (ISPCC), Solan, pp 50–55

  5. Zhang C, Ma LN, Jing LN (2014) Threshold infrared image segmentation based on improved genetic algorithm. In: IET international conference on information science and control engineering, IET, pp 1–4

  6. Sun W, Hu S, Liu S et al (2015) Infrared and visible image fusion based on object extraction and adaptive pulse coupled neural network via non-subsampled Shearlet transform. In: International conference on signal processing, IEEE, pp 946–951

  7. Mohd MRS, Herman SH, Sharif Z (2017) Application of K-means clustering in hot spot detection for thermal infrared images. In: IEEE symposium on computer applications and industrial electronics, IEEE, pp 107–110

  8. Neto JFSR, Braga AM, de Medeiros FNS, Marques RCP (2017) Level-set formulation based on Otsu method with morphological regularization. In: 2017 IEEE international conference on image processing (ICIP), Beijing, China, pp 2144–2148

  9. Ye H, Yan S, Huang P (2017) 2D Otsu image segmentation based on cellular genetic algorithm. In: 2017 IEEE 9th international conference on communication software and networks (ICCSN), Guangzhou, China, pp 1313–1316

  10. Zhao Y, Zhuhua H (2015) Image segmentation of fruits with diseases in natural scenes based on logarithmic similarity constraint Otsu. Trans Chin Soc Agric Mach 11:9–15

    Google Scholar 

  11. Zand M, Doraisamy S, Halin AA, Mustaffa MR (2015) Automatic object segmentation using perceptual grouping of regions with contextual constraints. In: 2015 international conference on image processing theory, tools and applications (IPTA), Orleans, pp 530–534

  12. Zhong W, Shen T (2013) Multiple features and SVM combined SAR image segmentation. Appl Res Comput 30(9):2846–2851

    Google Scholar 

  13. Zhang C, Li L, Dong Q (2016) Recognition for machine picking seed cotton impurities based on GA-SVM model. J Agric Eng 32(24):189–196

    Google Scholar 

  14. Lan R, Jia S (2016) A retrieval algorithm of forensic image fusion based on fusioning texture and color feature. J Xi’an Univ Posts Telecommun 21(2):57–62

    MathSciNet  Google Scholar 

  15. Lei B, Fan J (2016) An object extraction algorithm for forensic images based on minimum Chi square statistics. J Xi’an Univ Posts Telecommun 21(6):20–23

    Google Scholar 

  16. Min J, Chai Y (2016) A PCNN improved with fisher criterion for infrared human image segmentation. In: Advanced information technology, electronic and automation control conference, IEEE, pp 1101–1105

  17. Qu S, Yang H, Ma Z (2013) Infrared image target recognition of complex background based on curvelet neural network. In: Control conference, IEEE, pp 3560–3564

  18. Licheng J, Haifeng D (2003) Artificial immune system: progress and prospect. Acta Electron Sin 31(10):1540–1548 (in Chinese)

    Google Scholar 

  19. Jacob B, Balasubramanian K, Babu TS et al (2015) Parameter extraction of solar PV double diode model using artificial immune system. In: IEEE international conference on signal processing, informatics, communication and energy systems, IEEE, pp 1–5

Download references

Funding

This study was financially supported by National Natural Science Foundation of China (61502340) and Natural Science Foundation of Tianjin Municipal Science and Technology Commission (18JCQNJC01000).

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, and Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384, China

    Yu Xiao & Zhou Zijie

Authors
  1. Yu Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou Zijie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhou Zijie.

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

Xiao, Y., Zijie, Z. Infrared Image Extraction Algorithm Based on Adaptive Growth Immune Field. Neural Process Lett 51, 2575–2587 (2020). https://doi.org/10.1007/s11063-020-10218-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10218-7

Keywords

Search

Navigation