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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)

Abstract

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.

Keywords

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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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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