薛松, 韩广良. 基于旋转角预估的红外指定目标快速捕获[J]. 红外与激光工程, 2013, 42(11): 2907-2912.
引用本文: 薛松, 韩广良. 基于旋转角预估的红外指定目标快速捕获[J]. 红外与激光工程, 2013, 42(11): 2907-2912.
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.

基于旋转角预估的红外指定目标快速捕获

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

  • 摘要: 针对抗旋转的红外目标捕获问题,提出了一种基于旋转角预估的目标快速识别方法。该方法首先对图像进行局部自适应阈值的快速分割和连通域标记,采用积分图像加速计算;将每个连通域缩放至标准尺寸,以缩放后的二值图像的像素值作为特征,用神经网络估计目标的旋转角,并在角度估计过程中滤除一些非目标;最后,用神经网络进行目标识别。针对实际应用中样本量往往较小,模板与实际识别时可能存在差异的问题,提出了一种边缘随机生长和消去的样本生成方法。实验表明,提出的方法计算量小,在模板形状有少量偏差的情况下,仍能有较高的准确率。

     

    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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