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Classifying self-cast shadow regions in aerial camera images

机译:分类航拍图像中的自投射阴影区域

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In many fields of airborne surveillance, self-cast shadows (i.e. shadows in a scene which are cast by an aircraft) pose a not negligible problem for image processing tasks. Self-cast shadows can impede the stability of computer vision applications like remote sensing, visual odometry, or tracking tasks. In order to be able to reliably identify self-cast shadows in on-board camera images, a model-based approach has been developed. This approach utilizes easily accessible sensor data to make predictions about the position and the shape of self-cast shadows. The predicted shadow is then used to search for image regions which contain self-cast shadows. The developed approach is presented in detail in this paper. Further, the approach is applied on flight test data which has been recorded by a helicopter that is operated by the German Aerospace Center (DLR). The evaluation of the flight test shows that the approach is able to identify self-cast shadow regions with a high reliability.
机译:在空中监视的许多领域中,自铸阴影(即飞机所投射的场景中的阴影)在图像处理任务中构成了不可忽略的问题。自投阴影可能会妨碍计算机视觉应用程序(如遥感,视觉测距法或跟踪任务)的稳定性。为了能够可靠地识别车载摄像机图像中的自投阴影,已经开发了一种基于模型的方法。这种方法利用易于访问的传感器数据来预测自投影阴影的位置和形状。然后,将预测的阴影用于搜索包含自投射阴影的图像区域。本文详细介绍了已开发的方法。此外,该方法适用于由德国航空航天中心(DLR)运营的直升机记录的飞行测试数据。对飞行测试的评估表明,该方法能够高度可靠地识别自铸阴影区域。

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