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1. 发明申请

WO2007050707A2 VIDEO FOREGROUND SEGMENTATION METHOD 审中-公开
标题翻译：视频前缀分割方法
公开(公告)号：WO2007050707A2
公开(公告)日：2007-05-03
申请号：PCT/US2006/041672
申请日：2006-10-26
申请人： NEC LABORATORIES AMERICA, INC.
发明人： HAN, Mei , XU, Wei , GONG, Yihong
IPC分类号： G06K9/34
CPC分类号： G06K9/38 , G06T7/12 , G06T7/181 , G06T7/194 , G06T7/215 , G06T7/277 , G06T2207/10016 , H04N7/141
摘要： A fully automatic, computationally efficient segmentation method of video employing sequential clustering of sparse image features. Both edge and corner features of a video scene are employed to capture an outline of foreground objects and the feature clustering is built on motion models which work on any type of object and moving/static camera in which two motion layers are assumed due to camera and/or foreground and the depth difference between the foreground and background. Sequential linear regression is applied to the sequences and the instantaneous replacements of image features in order to compute affine motion parameters for foreground and background layers and consider temporal smoothness simultaneously. The Foreground layer is then extracted based upon sparse feature clustering which is time efficient and refined incrementally using Kalman filtering.
摘要翻译：一种使用稀疏图像特征的顺序聚类的全自动，计算效率高的视频分割方法。使用视频场景的边缘和角落特征来捕获前景对象的轮廓，并且特征聚类建立在对任何类型的对象和移动/静态相机工作的运动模型上，其中由于相机而假设两个运动层， /或前景和前景和背景之间的深度差。序列线性回归应用于图像特征的序列和瞬时替换，以便计算前景和背景层的仿射运动参数，同时考虑时间平滑度。然后基于稀疏特征聚类提取前景层，这是使用卡尔曼滤波进行时间有效和精确地提取的。

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