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    Minimax distance transform correlation filter based target detection in FLIR imagery

    机译:基于极大极小距离变换相关滤波器的FLIR图像目标检测

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    This paper proposes a method to detect objects of arbitrary poses and sizes from a complex forward looking infrared (FLIR) image scene exploiting image correlation technique along with the preprocessing of the scene using a class of morphological operators. This presented automatic target recognition (ATR) algorithm consists of two steps. In the first step, the image is preprocessed, by employing morphological reconstruction operators, to remove the background as well as clutter and to intensify the presence of both low or high contrast targets. This step also involves in finding the possible candidate target regions or region of interests (ROIs) and passing those ROIs to the second step for classification. The second step exploits template-matching technique such as minimax distance transform correlation filter (MDTCF) to identify the true target from the false alarms in the pre-selected ROIs after classification. The MDTCF minimizes the average squared distance from the filtered true-class training images to a filtered reference image while maximizing the mean squared distance of the filtered false-class training images to this filtered reference image. This approach increases the separation between the false-class correlation outputs and the true-class correlation outputs. Classification is performed using the squared distance of a filtered test image to the chosen filtered reference image. The proposed technique has been tested with real life FLIR image sequences supplied by the Army Missile Command (AMCOM). Experimental results, obtained with these real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.
    机译:本文提出了一种利用图像相关技术从复杂的前视红外(FLIR)图像场景中检测任意姿势和大小的对象的方法,以及使用一类形态学算子对场景进行预处理的方法。提出的自动目标识别(ATR)算法包括两个步骤。第一步,通过使用形态重建算子对图像进行预处理,以去除背景以及混乱,并增强低对比度或高对比度目标的存在。此步骤还涉及找到可能的候选目标区域或感兴趣区域(ROI),并将这些ROI传递到第二步进行分类。第二步利用模板匹配技术,例如最小最大距离变换相关滤波器(MDTCF),在分类后从预选ROI中的错误警报中识别出真实目标。 MDTCF使从滤波后的真实类训练图像到滤波后的参考图像的平均平方距离最小,同时使滤波后的伪类训练图像到该滤波后的参考图像的均方距离最大化。这种方法增加了假类相关输出和真类相关输出之间的间隔。使用过滤后的测试图像与所选过滤后的参考图像的平方距离执行分类。拟议的技术已通过陆军导弹司令部(AMCOM)提供的真实FLIR图像序列进行了测试。用这些真实的FLIR图像序列获得的实验结果说明了各种目标变化和杂波变化,证明了该方法的有效性和鲁棒性。

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