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Motion analysis and segmentation through spatio-temporal slices processing

机译:通过时空切片处理进行运动分析和分割

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This paper presents new approaches in characterizing and segmenting the content of video. These approaches are developed based upon the pattern analysis of spatio-temporal slices. While traditional approaches to motion sequence analysis tend to formulate computational methodologies on two or three adjacent frames, spatio-temporal slices provide rich visual patterns along a larger temporal scale. We first describe a motion computation method based on a structure tensor formulation. This method encodes visual patterns of spatio-temporal slices in a tensor histogram, on one hand, characterizing the temporal changes of motion over time, on the other hand, describing the motion trajectories of different moving objects. By analyzing the tensor histogram of an image sequence, we can temporally segment the sequence into several motion coherent subunits, in addition, spatially segment the sequence into various motion layers. The temporal segmentation of image sequences expeditiously facilitates the motion annotation and content representation of a video, while the spatial decomposition of image sequences leads to a prominent way of reconstructing background panoramic images and computing foreground objects.
机译:本文提出了表征和分割视频内容的新方法。这些方法是基于时空切片的模式分析而开发的。传统的运动序列分析方法倾向于在两个或三个相邻帧上建立计算方法,而时空切片则沿较大的时间尺度提供了丰富的视觉模式。我们首先描述一种基于结构张量公式的运动计算方法。该方法一方面编码张量直方图中的时空切片的视觉模式,另一方面表征运动随时间的时间变化,另一方面描述不同运动对象的运动轨迹。通过分析图像序列的张量直方图,我们可以将序列在时间上分割为几个运动连贯的子单元,此外,还可以在空间上将序列分割成各个运动层。图像序列的时间分割可快速促进视频的运动注释和内容表示,而图像序列的空间分解则导致一种突出的方式来重建背景全景图像和计算前景对象。

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