Experimental results show that the algorithm can accurately and robustly segment the video object.
实验结果表明，本文提出的算法能精确、鲁棒地分割出视频对象。
Motion features in video can usually be divided into two types. They are global motion, which is caused by camera motion, and local motion, which is caused by video object motion.
视频中的运动特征通常可以分为全局运动和局部运动两种，分别表示摄像机运动和视频对象运动的特征。
This thesis is focused on semiautomatic video object segmentation and tracking.
本文主要研究半自动的视频对象分割与跟踪。
Object-based video coding mostly includes video object segmentation and video object coding.
基于对象的视频编码主要包括视频对象的分割和视频对象的编码两部分。
It decomposes sequences into video object planes (VOPs) so that each VOP represents one moving object.
它把图像序列分解成视频对象平面(VOP)，每个VOP代表一个运动对象。
The well-known "singularity" problem is one of the major defects in kernel-based video object tracking.
“无穷多解”问题，是基于核函数的视频物体跟踪算法的主要问题。
Automatic extraction of motion region in video sequence is critical to the generation of a video object.
视频运动区域的自动提取是视频对象生成的关键。
The performance of video object segmentation algorithm has the direct effect on quality of MPEG-4 products.
视频对象分割算法的性能好坏将直接影响MPEG-4编码产品的质量。
A robust and novel method for video object contour tracking by improving the dual-front active contour model.
对双前沿活动轮廓模型进行了改进，提出一种鲁棒的视频目标轮廓跟踪新方法。
The system is running in the four states to accomplish the detection and tracking of the moving video object.
系统按照搜索、捕获、跟踪、记忆跟踪四个状态及其转换运行，以实现运动目标的实时检测与跟踪。
For the problem of video object segmentation, a new approach was presented to video object extraction and tracking.
针对视频对象的分割问题，提出了一种新的视频对象提取与跟踪方法。
MPEG-4 standards are described in brief. Based on this, audio object coding and video object coding are emphasized.
简要介绍了MPEG - 4标准的主要内容在此基础上着重介绍了音频对象的编码和视频对象的编码。
Recently, as a difficult problem in video object tracking non-rigid object tracking has received much more attention.
近年来，非刚体目标跟踪技术作为视频目标跟踪中的一个难点受到了广泛关注。
The video object generated in this paper utilizes the technology of motion-changed region detection and edge detection.
通过采用运动变化区域检测和边缘检测技术来生成视频对象。
This paper presents a video model suited for video object composition and analyzes the structured feature of video object.
提出适合视频对象合成的结构化视频模型，分析视频对象的结构特点。
Finally, we applied heuristic search method to link video object contour's edge points, sequentially extracted video object.
最后，采用启发式搜索方法连接视频对象轮廓边缘点，进而提取出视频对象。
The result of experiment shows that this algorithm can pull foreground video object from a complicated background effectively.
实验结果表明，该算法可以有效实现对复杂背景下前景目标的提取。
An algorithm extracting video object is proposed by utilizing the HOS (higher order statistics) and multi-resolution watershed algorithm in video sequences.
提出了将高阶统计运动检测和多尺度分水岭相结合的视频目标分割算法。
Video content is described by characteristics extracted on four-level framework including shots, video objects, video object planes and video object regions.
本文提出了一种基于视频对象的视频内容分级描述模型。
A video object watermark algorithm using Scale-Invariant feature Transform (SIFT) features and video segmentation based on spatiotemporal information is proposed.
提出一种结合尺度不变特征变换(SIFT)特征点校正和时空域视频对象分割的视频对象水印算法。
Experimental results demonstrate that this method can effectively separate the video object from the sequence with static background and has the strong robustness.
实验结果证明，该方法能够从背景不变的图像序列中较好的提取出运动对象，具有较强的鲁棒性。
Experimental results on typical test sequences demonstrate that the proposed approach can achieve pixel-level accuracy for com-pressed-domain video object extraction.
典型视频测试序列的实验结果表明，本方法可取得接近于像素级的分割精度。
In order to support content based functions in the visual standard of MPEG 4, each frame of video sequences should be represented in terms of video object planes (VOP's).
在MPEG -4视频编码标准中，为了实现基于视频内容的交互功能，其视频序列的每一帧由视频对象面(VOP)来表示。
This paper introduces video segmentation of temporal segmentation and spatial segmentation, presents a video object segmentation algorithm based on multi-frames difference.
文章介绍了基于内容的时域及空域视频分割技术，提出了一种基于多帧差异的视频对象分割算法。
The segmentation of video is the basis of the video object extraction, processing and recognition. It is also the premise of the content-based video compression and retrieving.
视频序列分割是实现视频对象提取、处理和识别的基础，也是基于内容的视频压缩和检索的前提。
In this thesis, MPEG-4 model-based coding for facial video object is investigated. The whole codec system is generally discussed and divided into two parts of Analysis and Synthesis.
本文研究了MPEG - 4中人脸视频对象的模型基编码，对整个系统进行了全面的分析，将系统按编解码结构分成分析和合成两部分。
This paper makes a deep insight into two problems of object-based fine granular scalable video coding in the wavelet domain, namely video object coding and motion estimation techniques.
本文对基于对象的小波域视频细粒度可分级编码中的两个关键问题，即视频对象编码和运动估计技术进行了深入研究。
As to automatic segmentation, these techniques often do not obtain desirable segmentation results because the mathematic model required for the extraction of a video object is difficult to define.
对视频对象自动分割来说，因为提取视频对象所需要的数学模型很难定义，这些技术通常不能获得理想的分割结果。
Video object segmentation is the one of the focus in the field of image and video processing.
视频对象分割是当前图像和视频处理的热点和难点之一。
Video object segmentation is the one of the focus in the field of image and video processing.
视频对象分割是当前图像和视频处理的热点和难点之一。
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