Volume 42 Issue 11
Feb.  2014
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Xue Song, Han Guangliang. Fast capture of appointed infrared targets based on estimation of rotation angle[J]. Infrared and Laser Engineering, 2013, 42(11): 2907-2912.
Citation: Xue Song, Han Guangliang. Fast capture of appointed infrared targets based on estimation of rotation angle[J]. Infrared and Laser Engineering, 2013, 42(11): 2907-2912.

Fast capture of appointed infrared targets based on estimation of rotation angle

  • Received Date: 2013-03-11
  • Rev Recd Date: 2013-04-07
  • Publish Date: 2013-11-25
  • For the problem of recognizing infrared targets, a method based on estimation of rotation angle was proposed. The method first segmented the image by local adaptive threshold and mark connected areas. Integral image was used to accelerate the computation of the local threshold. The connected areas were resized to the same size. The pixel values of the resized images were used as features. Then the rotation angle of the target was estimated by a neural network. Some areas were filtered during the estimation. At last, the area was recognized by another neural network. For some applications, the number of sample was not sufficient and a little difference existed between the samples and targets. For this problem, a method based on random growth and erosion was proposed to generate samples. Experiments show the method is effective and has a high recognizing rate even when the shape of samples is not exact.
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Fast capture of appointed infrared targets based on estimation of rotation angle

  • 1. Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;
  • 2. University of Chinese Academy of Sciences,Beijing 100049,China

Abstract: For the problem of recognizing infrared targets, a method based on estimation of rotation angle was proposed. The method first segmented the image by local adaptive threshold and mark connected areas. Integral image was used to accelerate the computation of the local threshold. The connected areas were resized to the same size. The pixel values of the resized images were used as features. Then the rotation angle of the target was estimated by a neural network. Some areas were filtered during the estimation. At last, the area was recognized by another neural network. For some applications, the number of sample was not sufficient and a little difference existed between the samples and targets. For this problem, a method based on random growth and erosion was proposed to generate samples. Experiments show the method is effective and has a high recognizing rate even when the shape of samples is not exact.

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