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Depth gradient based segmentation of overlapping foreground objects in range images

机译:基于深度梯度的范围图像中的重叠前景对象的分割

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Using standard background modeling approaches, close or overlapping objects are often detected as a single blob. In this paper we propose a new and effective method to distinguish between overlapping foreground objects in data obtained from a time of flight sensor. For this we use fusion of the infrared and the range data channels. In addition a further processing step is introduced to evaluate if connected components should be further divided. This is done using nonmaximum suppression on strong depth gradients.
机译:使用标准后台建模方法,闭合或重叠对象通常被检测为单个BLOB。在本文中,我们提出了一种新的有效方法,以区分从飞行传感器时间获得的数据中的重叠的前景对象。为此,我们使用红外和范围数据通道的融合。另外,引入进一步的处理步骤以评估连接的组件是否应进一步分开。这是在强度深度梯度上使用非含量抑制完成的。

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