王恩国, 高印寒, 苏成志, 刘妍妍. 小目标图像局部极值收敛提取算法[J]. 红外与激光工程, 2014, 43(4): 1352-1358.
引用本文: 王恩国, 高印寒, 苏成志, 刘妍妍. 小目标图像局部极值收敛提取算法[J]. 红外与激光工程, 2014, 43(4): 1352-1358.
Wang Enguo, Gao Yinhan, Su Chengzhi, Liu Yanyan. Extraction of small target based on local extreme convergence[J]. Infrared and Laser Engineering, 2014, 43(4): 1352-1358.
Citation: Wang Enguo, Gao Yinhan, Su Chengzhi, Liu Yanyan. Extraction of small target based on local extreme convergence[J]. Infrared and Laser Engineering, 2014, 43(4): 1352-1358.

小目标图像局部极值收敛提取算法

Extraction of small target based on local extreme convergence

  • 摘要: 复杂背景下小目标自动提取技术尚不够完善,针对小目标自动提取问题,提出一种通过汇聚于同一极值点的所有路径来描述小目标区域的目标提取算法。该算法根据图像梯度特性筛选出路径的起始点位置;从起始点位置出发,沿着梯度最速下降方向,使路径收敛于图像的局部极小值点,收敛于同一极值点的所有路径构成独立核心区域;分析了焦点独立核心区域和噪声独立核心区域特征间的差异,以该特征中的内外均值比特征,对独立核心区域进行了滤波,分离出焦点核心区域;对独立核心区域聚合,得到目标核心区域。通过实验证明:该算法能够自动检测出焦点目标,和现有算法相比,提高了小目标提取的自动化程度。

     

    Abstract: Automatic detection of small targets in the complex context is still not perfect, an algorithm was proposed that used all paths converging to the same limit point to describe the small target area. The starting points of the path were screened based on the image gradient features. A path starting from the starting point along the gradient direction of steepest descent converged to a local minimum point, and all the paths that converge in the same path constituted an independent core region. The difference was analyzed in the target features between the focus independent core region and noise independent region, and the gray average ratio of the target features inside and outside was used for the independent core area filtering, the focus of the core area was obtained. The region of the target core was obtained by polymerizing the independent core region. The experiments show that the algorithm can automatically detect the focus target, and compared with existing algorithms, it increases the degree of automation of the small object extraction, have a strong robustness.

     

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