Real-Time Detection of Small Surface Objects Using Weather Effects

  • Baojun Qi
  • Tao Wu
  • Hangen He
  • Tingbo Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Small surface objects, usually containing important information, are difficult to be identified under realistic atmospheric conditions because of weather degraded image features. This paper describes a novel algorithm to overcome the problem, using depth-aware analysis. Because objects-participating local patches always contain low intensities in at least one color channel, we detect suspicious small surface objects using the dark channel prior. Then, we estimate the approximate depth map of maritime scenes from a single image, based on the theory of perspective projection. Finally, using the estimated depth map and the atmospheric scattering model, we design spatial-variant thresholds to identify small surface objects from noisy backgrounds, without contrast enhancement. Experiments show that the proposed method has real-time implementation, and it can outperform the state-of-the-art algorithms on the detection of distant small surface objects with only a few pixels.


Surface Object Saliency Detection Horizontal Edge Perspective Projection Scene Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Achanta, R., Hemami, S., Estrada, F., Süsstrunk, S.: Frequency-tuned salient region detection. In: CVPR (2009)Google Scholar
  2. 2.
    Chan, A.B.: Beyond Dynamic Textures: a Family of Stochastic Dynamical Models for Video with Applications to Computer Vision. PhD thesis, University of California, San Diego (2008)Google Scholar
  3. 3.
    Cozman, F., Krotkov, E.: Depth from scattering. In: CVPR, pp. 801–806 (1997)Google Scholar
  4. 4.
    Fattal, R.: Single image dehazing. In: SIGGRAPH, pp. 1–9 (2008)Google Scholar
  5. 5.
    Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall, Englewood Cliffs (2003)Google Scholar
  6. 6.
    Gupta, K.M., Aha, D.W., Moore, P.: Case-based collective inference for maritime object classification. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS, vol. 5650, pp. 434–449. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: CVPR, pp. 1956–1963 (2009)Google Scholar
  8. 8.
    Herk, M.V.: A fast algorithm for local minimum and maximum filters on rectangular and octogonal kernels. Pattern Recognition Letters 13, 517–521 (1992)CrossRefGoogle Scholar
  9. 9.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: CVPR (2007)Google Scholar
  10. 10.
    Mallat, S., Hwang, W.: Singularity detection and processing with wavelets. IEEE Transactions on Information Theory 38, 617–643 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    McCartney, E.J.: Optics of the Atmosphere–Scattering by Molecules and Particles. John Wiley and Sons Inc., Chichester (1976)Google Scholar
  12. 12.
    Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. International Journal of Computer Vision 48, 233–254 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 713–724 (2003)CrossRefGoogle Scholar
  14. 14.
    Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: ICCV, pp. 820–827 (1999)Google Scholar
  15. 15.
    Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Sanderson, J., Teal, M., Ellis, T.: Target identification in complex maritime scenes. In: The Sixth International Conference on Image Processing and its Applications, vol. 2, pp. 463–467 (1997)Google Scholar
  17. 17.
    Smith, A., Teal, M., Voles, P.: The statistical characterization of the sea for the segmentation of maritime images. In: The 4th EC-VIP-MC, vol. 2, pp. 489–494 (2003)Google Scholar
  18. 18.
    Sullivan, M.D.R., Shah, M.: Visual surveillance in maritime port facilities. In: Visual Information Processing XVII, pp. 1–8 (2008)Google Scholar
  19. 19.
    Tan, R.: Visibility in bad weather from a single image. In: CVPR, pp. 1–8 (2008)Google Scholar
  20. 20.
    Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters 27, 773–780 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Baojun Qi
    • 1
  • Tao Wu
    • 1
  • Hangen He
    • 1
  • Tingbo Hu
    • 1
  1. 1.Institute of Automation, College of Mechatronics Engineering and AutomationNational University of Defense TechnologyChangshaP.R. China

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