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METHODS AND SYSTEMS FOR CLASSIFYING PIXELS AS FOREGROUND USING BOTH SHORT-RANGE DEPTH DATA AND LONG-RANGE DEPTH DATA

机译:使用短距离深度数据和长距离深度数据将像素分类为复合像素的方法和系统

摘要

Disclosed herein are methods and systems for classifying pixels as foreground using both short-range depth data and long-range depth data. One embodiment takes the form of a process that includes obtaining video data depicting at least a portion of a user. The process also includes obtaining short-range depth data associated with the video data. The process also includes obtaining long-range depth data associated with the video data. The video data, short-range depth data, and long-range depth data may be obtained via a single 3-D video camera. The process also includes classifying pixels of the video data as foreground based at least in part on both the short-range depth data and the long-range depth data. In some embodiments, classifying pixels of the video data as foreground comprises employing an alpha mask. The alpha mask may comprise binary foreground (hard) indicators. The alpha mask may comprise foreground-likelihood (soft) indicators.
机译:本文公开了用于使用短距离深度数据和长距离深度数据两者将像素分类为前景的方法和系统。一个实施例采用一种过程的形式,该过程包括获得描绘用户的至少一部分的视频数据。该过程还包括获得与视频数据相关联的短程深度数据。该过程还包括获得与视频数据相关联的远程深度数据。视频数据,近距离深度数据和远距离深度数据可以通过单个3D摄像机获得。该过程还包括至少部分地基于短距离深度数据和长距离深度数据两者将视频数据的像素分类为前景。在一些实施例中,将视频数据的像素分类为前景包括采用阿尔法掩模。阿尔法掩模可以包括二进制前景(硬)指示符。阿尔法掩模可以包括前景似然(软)指示符。

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