This paper represents a simple moving foreground segmentation method in video sequences only using their temporal information.
针对视频序列，仅利用其时域信息，提出了一种简单有效的运动前景分割算法。
This algorithm consists of foreground segmentation, human head location, human body parts identification, human model construction etc.
该算法主要包括背景建模与前景提取、人体头部定位、人体肢体提取和人体建模几个部分。
Modify the segmentation function so that it only has minima at the foreground and background marker locations.
修改分割函数，使其仅在前景和后景标记位置有极小值。
In object detection of high frequency sonar image, it needs to get the parameters of foreground objects after image segmentation.
在高频声纳图像目标检测中，对图像分割后，需要对前景目标参数进行提取。
The method consists of three steps. Firstly, image segmentation is achieved by watershed transform based on phase congruency gradient and foreground marking to extract image objects.
首先，利用基于相位一致梯度与前景标记的分水岭变换进行影像分割，提取图像斑块；
In order to reduce adverse effect of image segmentation in RBIR, a new method based on partition of foreground and background is proposed.
为了降低RBIR中图像分割的影响，提出了一种基于前景和背景划分的区域图像检索方法。
Finally, we performed morphological filtering on the segmentation results, in order to eliminate the miscellaneous points and restore the moving foreground of the image sequences.
最后对分割结果进行了形态滤波，以此来消除杂点等，恢复出图像序列中的运动前景。
Finally, we proposed a rate control scheme which is appropriate for the foreground and background segmentation encoding.
最后，本文提出了一种适用于前后景分割编码的码率控制算法。
Then the result of the original segmentation is improved by some extra processing. After that, a special method is employed to distinguish the foreground regions and background regions.
而后 ，经过一系列的后处理来优化分割结果 ，并实现前景和背景区分 ；
Then the result of the original segmentation is improved by some extra processing. After that, a special method is employed to distinguish the foreground regions and background regions.
而后 ，经过一系列的后处理来优化分割结果 ，并实现前景和背景区分 ；
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