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Target localization by least squares image matching including the deconvolution of image blur

机译:目标本地化至少方块图像匹配包括图像模糊的去卷积

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For many applications in photogrammetry precise target localization is essential. Target localization by image processing is composed of several steps, from target detection to fine centering at the end of the process. The following paper describes a model for fine centering of a target based on Least Squares Image Matching (LSM). Since the image of a target has been convoluted by the image sensor LSM has to be extended by an appropriate mathematical model. Its derivation, together with practical results for fiducial marks (circles and crosses) and targets that are circular in object space, are presented.
机译:对于摄影测量中的许多应用,精确的目标本地化至关重要。通过图像处理的目标本地化由几个步骤组成,从目标检测到过程结束时的精确居中。下文介绍了基于最小二乘图像匹配(LSM)的目标的精细居中的模型。由于目标的图像已经被图像传感器LSM卷积,因此必须通过适当的数学模型扩展。它的推导率与基准标记(圆圈和交叉)的实际结果和在物体空间中的圆形圆形的实际结果一起提出。

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