Advertisement

Detecting Infrared Target with Receptive Field and Lateral Inhibition of HVS

  • Yufei Zhao
  • Yong Song
  • Shangnan Zhao
  • Yun Li
  • Guowei Shi
  • Zhengkun Guo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

In this paper, we proposed an infrared (IR) target detection method based on the receptive field (RF) and lateral inhibition (LI). In this method, the direction parameters of Gabor filter is adaptively determined according to the gradient direction. And a background prediction method based on LI is used for regulating the gray value in image so as to achieve background suppression and target enhancement. Experimental results indicate that the proposed method can extract both small and area target from complex background, and the target detection ability is satisfactory.

Keywords

Detection Infrared Digital image processing Pattern recognition 

Notes

Acknowledgement

This work is supported by National Natural Science Foundation of China (NSFC) (81671787); Defense Industrial Technology Development Program (JCKY2016208B001).

References

  1. 1.
    Venkateswarlu, R.: Max-mean and max-median filters for detection of small targets. Proc. SPIE - Int. Soc. Opt. Eng. 3809, 74–83 (1999)Google Scholar
  2. 2.
    Yang, L., Yang, J., Yang, K.: Adaptive detection for infrared small target under sea-sky complex background. Electron. Lett. 40(17), 1083–1085 (2004)CrossRefGoogle Scholar
  3. 3.
    Yang, C., Ma, J., Qi, S., Tian, J., Zheng, S., Tian, X.: Directional support value of gaussian transformation for infrared small target detection. Appl. Opt. 54(9), 2255–65 (2015)CrossRefGoogle Scholar
  4. 4.
    Soni, T., Zeidler, J.R., Ku, W.H.: Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data. IEEE Trans. Image Process. A Publ. IEEE Sign. Process. Soc. 2(3), 327 (1993)CrossRefGoogle Scholar
  5. 5.
    Wang, X., Lv, G., Xu, L.: Infrared dim target detection based on visual attention. Infrared Phys. Technol. 55(6), 513–521 (2012)CrossRefGoogle Scholar
  6. 6.
    Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A Opt. Image Sci. 2(7), 1160–1169 (1985)CrossRefGoogle Scholar
  7. 7.
    Hartline, H.K.: The response of single optic nerve fibers of the vertebrate eye to illumination of the retina. Am. J. Physiol. 121(2), 400–415 (1938)Google Scholar
  8. 8.
    Dai, S., Liu, Q., Li, P., Liu, J., Xiang, H.: Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network. Infrared Phys. Technol. 68, 10–14 (2015)CrossRefGoogle Scholar
  9. 9.
    Zhang, W., Cong, M., Wang, L.: Algorithms for optical weak small targets detection and tracking: review. In: International Conference on Neural Networks and Signal Processing, vol. 1, pp. 643–647 (2004)Google Scholar
  10. 10.
    Shi, M., Peng, Z., Zhang, Q., Li, Q., Lin, Z.: Dim infrared target detection based on adaptive lateral inhibition network. High Power Laser Part. Beams 23(4), 906–910 (2011)CrossRefGoogle Scholar
  11. 11.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour detection based on nonclassical receptive field inhibition. IEEE Trans. Image Process. A Public. IEEE Sign. Process. Soc. 12(7), 729–739 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yufei Zhao
    • 1
    • 2
  • Yong Song
    • 1
    • 2
  • Shangnan Zhao
    • 1
    • 2
  • Yun Li
    • 1
    • 2
  • Guowei Shi
    • 3
  • Zhengkun Guo
    • 1
    • 2
  1. 1.School of Optics and photonicsBeijing Institute of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and TechnologyBeijingChina
  3. 3.Institute of Aviation MedicineAF CPLABeijingChina

Personalised recommendations