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Detecting a small target in an IR image by eliminating blocks on ground using successive overrelaxation

机译:通过连续的超额来消除地面上的块检测IR图像中的小目标

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A method for automatic segmentation and detection of small target in the earth to sky background was presented in this paper. When small target in far distance some objects on ground came upon to the scene. This always resulted in false detection in automatic detecting systems if we segmented small object by only using its illumination. In order to remove the big block and disconnected part of a gradient image was made because the gradient scale of the small target and background objects may be comparatively similar. Then an adaptive threshold method was adopted by using the image means as a threshold for several times to segment objects in the gradient image. After that successive over-relaxation was used to incorporate the disperse regions and the nearby isolated points would connect to the big block. The lowest value of valley (nonzero value) was searched and this value was used as the threshold to make binary image. So the connected clutters could be removed only by counting the number of pixels in the connected regions. Eventually the pipeline target detection algorithm was used to process the sequential images to detect the real small target automatically.
机译:一种用于自动分割,并在地球的天空背景小目标检测方法在本文中被提出。当小目标在远处地面上的一些对象临到现场。这总是导致自动检测系统的误检测,如果我们仅使用其照明分割的小物体。为了除去大块和梯度图像的断开部分被做是因为小目标和背景物体的梯度规模可能比较相似。然后,一个自适应阈值的方法是通过使用图像的装置作为用于多次分段对象的梯度图像中的阈值通过。历届超松弛使用后纳入分散地区和附近的隔离点,将连接到大的块。谷(非零值)的最低值被搜查,该值被用作阈值,使二值图像。因此,连接杂波可以通过在连通区域计数的像素数仅去除。最终使用的管道目标检测算法来处理顺序图像来自动检测真实小目标。

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