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IR small target detection based on human visual attention using pulsed discrete cosine transform

IR small target detection based on human visual attention using pulsed discrete cosine transform

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Detection of small targets in an infrared (IR) image with high reliability is very important for defence systems. Small targets in an IR image are defined as salient features which attract the attention of human visual system. In this study, a robust method for detection of small targets in an IR image is proposed based on HV attention. In this method, first, the Gaussian-like feature maps are extracted from the original image. Then, saliency maps (SMs) are created based on pulsed discrete cosine transform, in which the target is salient and background clutter is suppressed. Finally, to increase the contrast between target and background clutter and to raise robustness of this method against false alarms, SMs are fused adaptively. Experiments are carried out on the data set including real-life IR images with small targets as well as various and complicated backgrounds. Qualitative and quantitative assessments show that the proposed method can detect small targets in IR image with high reliability and is more effective compared with other methods based on HV attention. Therefore, it can be used in many applications for detection of small targets in IR image with minimum false alarms.

References

    1. 1)
      • 1. Deshpande, S., Meng, H.E., Ronda, V., et al: ‘Max-mean and max-median filters for detection of small targets’, Proc. SPIE, 1999, 3809, pp. 7483.
    2. 2)
      • 2. Bai, X., Zhou, F.: ‘Analysis of new top-hat transformation and the application for infrared dim small target detection’, Pattern Recognit.., 2010, 43, (6), pp. 21452156.
    3. 3)
      • 3. Zhao, J., Tang, Z., Yang, J., et al: ‘Infrared small target detection using sparse representation’, J. Syst. Eng. Electron., 2011, 22, pp. 897904.
    4. 4)
      • 4. Bae, T.W.: ‘Spatial and temporal bilateral filter for infrared small target enhancement’, Infrared Phys. Technol., 2014, 63, pp. 4253.
    5. 5)
      • 5. Bae, T., Zhang, F., Kweon, I.: ‘Edge directional 2D LMS filter for infrared small target detection’, Infrared Phys. Technol., 2012, 55, (1), pp. 137145.
    6. 6)
      • 6. Deng, H., Liu, J.: ‘Infrared small target detection based on the self-information map’, Infrared Phys. Technol., 2011, 54, (2), pp. 100107.
    7. 7)
      • 7. Yang, L., Zhou, Y., Yang, J., et al: ‘Variance WIE based infrared images processing’, Electron. Lett., 2006, 42, (15), pp. 857859.
    8. 8)
      • 8. Wang, G.D., Chen, C.Y., Shen, X.B.: ‘Facet-based infrared small target detection method’, Electron. Lett., 2005, 41, (22), pp. 12441246.
    9. 9)
      • 9. Haralick, R.: ‘Digital step edges from zero crossing of second directional derivatives’, IEEE Trans. Pattern Anal. Mach. Intell., 1984, 6, (1), pp. 5868.
    10. 10)
      • 10. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (11), pp. 12541259.
    11. 11)
      • 11. Borji, A., Itti, L.: ‘State-of-the-art in visual attention modeling’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (1), pp. 185207.
    12. 12)
      • 12. Chen, C.P., Li, H., Wei, Y., et al: ‘A local contrast method for small infrared target detection’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (1), pp. 574581.
    13. 13)
      • 13. Han, J., Ma, Y., Zhou, B., et al: ‘A robust infrared small target detection algorithm based on human visual system’, IEEE Trans. Geosci. Remote Sens. Lett., 2014, 11, (12), pp. 21682172.
    14. 14)
      • 14. Qi, S., Ming, D., Ma, J., et al: ‘Robust method for infrared small-target detection based on Boolean map visual theory’, Appl. Opt., 2014, 53, (18), pp. 39293940.
    15. 15)
      • 15. Nasiri, M., Mosavi, M.R., Mirzakuchaki, S.: ‘Infrared dim small target detection with high reliability using saliency map fusion’, IET Image Process., 2016, 10, (7), pp. 110, doi: 10.1049/iet-ipr.2015.0744.
    16. 16)
      • 16. Hou, X., Zhang, L.: ‘Saliency detection: a spectral residual approach’. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, CVPR20, 2007, pp. 18.
    17. 17)
      • 17. Guo, C., Ma, Q., Zhang, L.: ‘Spatiotemporal saliency detection using phase spectrum of quaternion Fourier transform’. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR2008), 2008, pp. 18.
    18. 18)
      • 18. Yu, Y., Wang, B., Zhang, L.: ‘Pulse discrete cosine transform for saliency-based visual attention’. Proc. of Eighth Int. Conf. on Development and Learning (ICDL2009), 2009, pp. 16.
    19. 19)
      • 19. Peters, R., Itti, L.: ‘The role of Fourier phase information in predicting saliency’, J. Vis., 2008, 8, (6), p. 879.
    20. 20)
      • 20. Achanta, R., Hemami, S., Estrada, F., et al: ‘Frequency-tuned salient region detection’. IEEE Conf. on Computer Vision and Pattern Recognition, 2009, pp. 15971604.
    21. 21)
      • 21. Qi, S., Ma, J., Tao, C., et al: ‘A robust directional saliency-based method for infrared small-target detection under various complex backgrounds’, IEEE Lett. Geosci. Remote Sens., 2013, 10, pp. 495499.
    22. 22)
      • 22. Qi, S., Ma, J., Li, H., et al: ‘Infrared small target enhancement via phase spectrum of quaternion Fourier transform’, Infrared Phys. Technol., 2014, 62, pp. 5058.
    23. 23)
      • 23. Wang, X., Guofang, L., Lizhong, X.: ‘Infrared dim target detection based on visual attention’, Infrared Phys. Technol., 2012, 55, (6), pp. 513521.
    24. 24)
      • 24. Yu, Y., Wang, B., Zhang, L.: ‘Bottom–up attention: pulsed PCA transform and pulsed cosine transform’, Cogn. Neurodyn., 2011, 5, (4), pp. 321332.
    25. 25)
      • 25. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91110.
    26. 26)
      • 26. Ahmed, N., Natarajan, T., Rao, K.: ‘Discrete cosine transform’, IEEE Trans. Comput., 1974, 23, pp. 9093.
    27. 27)
      • 27. Fang, Y., Chen, Z., Lin, W., et al: ‘Saliency detection in the compressed domain for adaptive image retargeting’, IEEE Trans. Image Process., 2012, 21, (9), pp. 38883901.
    28. 28)
      • 28. Fang, Y., Lin, W., Chen, Z., et al: ‘A video saliency detection model in compressed domain’, IEEE Trans. Image Process., 2014, 24, (1), pp. 2738.
    29. 29)
      • 29. Fang, Y., Wang, Z., Lin, W., et al: ‘Video saliency incorporating spatiotemporal cues and uncertainty weighting’, IEEE Trans. Image Process., 2014, 23, (9), pp. 39103921.
    30. 30)
      • 30. Zhang, W., Cong, M., Wang, L.: ‘Algorithms for optical weak small targets detection and tracking: review’. IEEE Conf. on Neural Networks and Signal Processing, 2003, vol. 1, pp. 643647.
    31. 31)
      • 31. Hilliard, C.: ‘Selection of a clutter rejection algorithm for real-time target detection from an airborne platform’. SPIE Conf. on Signal and Data Processing of Small Targets, 2000, pp. 7484.
    32. 32)
      • 32. Gu, Y., Wang, C., Liu, B., et al: ‘A kernel-based nonparametric regression method for clutter removal in infrared small-target detection applications’, IEEE Geosci. Remote Sens. Lett., 2010, 7, (3), pp. 469473.
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