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Ai Rui, Shi Zelin, Zhang Chengshuo. Line segment detection in low-SNR infrared image[J]. Infrared and Laser Engineering, 2013, 42(1): 278-284.
Citation: Ai Rui, Shi Zelin, Zhang Chengshuo. Line segment detection in low-SNR infrared image[J]. Infrared and Laser Engineering, 2013, 42(1): 278-284.

Line segment detection in low-SNR infrared image

  • Received Date: 2012-05-22
  • Rev Recd Date: 2012-06-19
  • Publish Date: 2013-01-25
  • Line segment detection in low-SNR infrared image is difficult as the gradient field calculation is badly affected by noise. In this paper, a new approach was presented to distinguish line elements and non-line ones in an image according to the fact that gradient field phase distribution of a line area distinctly appears non-isotropic. Local non-isotropy was defined and a calculating operator was presented. By combing phase-grouping algorithm and contrario detection framework, a new line segments detection algorithm was proposed. Experimental results indicate that the algorithm can effectively detect line segments in low-SNR images with a low false alarm rate.
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Line segment detection in low-SNR infrared image

  • 1. Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;
  • 2. University of Chinese Academy of Sciences,Beijing 100049,China;
  • 3. Key Laboratory of Opto-Electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China

Abstract: Line segment detection in low-SNR infrared image is difficult as the gradient field calculation is badly affected by noise. In this paper, a new approach was presented to distinguish line elements and non-line ones in an image according to the fact that gradient field phase distribution of a line area distinctly appears non-isotropic. Local non-isotropy was defined and a calculating operator was presented. By combing phase-grouping algorithm and contrario detection framework, a new line segments detection algorithm was proposed. Experimental results indicate that the algorithm can effectively detect line segments in low-SNR images with a low false alarm rate.

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