针对物体基视频编码与分割应用，提出了求取特征区域真实运动矢量的一种算法。
Proposes an algorithm to determine the true motion vectors of the feature regions for object based video coding and segmentation applications.
通过变化检测，映射参数估算，运动区域和背景检测来实现运动分割。
Change detection, mapping parameter estimation, moving objects and background detection are used to implement image motion segmentation.
首先，利用运动检测器将图像序列的每一帧分割成静止区域与运动区域。
Firstly, each frame in image sequence is distinguished between stationary and moving regions by using a motion detector.
分割区域用运动和位置参数定义，这些参数被称为映射参数。
An area to be segmented is defined by a set of uniform motion and position parameters denoted as mapping parameters.
对于待跟踪的运动目标，采用三帧差和区域增长法分割目标并得到主颜色信息。
We used frame difference and region growing method to divide target and get main color information.
该方法先在静态图像中分割出感兴趣区域，继而仅在感兴趣区域中应用变形的时间差分法来检测运动目标。
Those regions of interest are quickly segmented first and then a modified temporal differencing method is applied to detecting the moving objects.
针对基于对象的视频编码应用，提出了一种基于运动的区域生长分割方案，将图像分割成具有一致运动特征的区域。
This paper proposes a motion based region growing segmentation scheme for the object based video coding which segments an image into homogeneous regions characterized by a coherent motion.
视频运动对象分割的目标是从视频帧序列中间分割出满足一定特征的语义区域。
Segmentation of video moving object is extraction of semantic area meet certain characteristics from video sequence.
在定位分割出上半人脸运动单元子区域图像之后，提出了采用KPCA算法提取它们的特征。
After upper facial action unit location and segmentation, we present the facial action unit feature extraction algorithm based on KPCA.
还结合序列图像的相关性，运用免疫计算方法，分割出目标运动区域，形成目标模板，然后在运动序列的各帧动态更新模板。
In light of sequence images' relevance we have utilized immune algorithm to segment object's moving areas and form object templates.
而可以综合集成图像的区域、边缘、形状、纹理、以及运动场等信息的变分方法因其良好的通用性与可扩展性成为时下图像分割领域主流的方法之一。
Moreover, variational method which can conveniently integrate image information, such as edge, region, shape, texture and motion field becomes the mainstream method of image Segmentation.
给出连续图像帧差分和二次帧差分改进的图像HSI差分模型，采用自适应分割算法能在任意条件下自动提取运动目标区域。
It has been focused in compute vision research fields. A improved HSI image difference model based on sequences image difference and second image difference is presented.
给出连续图像帧差分和二次帧差分改进的图像HSI差分模型，采用自适应分割算法能在任意条件下自动提取运动目标区域。
It has been focused in compute vision research fields. A improved HSI image difference model based on sequences image difference and second image difference is presented.
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