首先，进行视频分割和提取关键帧。
视频分割是目标识别的关键技术。
Video segmentation is one of the key technique of the object recognition.
本文概述了基于内容的视频分割技术。
This article has outlined based on the content video frequency division technology.
该文讨论面向对象编码的视频分割算法。
This paper deals with automatic object segmentation method for object-oriented video encoding.
如何识别镜头渐变是视频分割中的难点。
Identifying gradual change in video sequences is the difficult point in video shot cut detection.
首先讨论和建立了视频分割的一般理论模型。
First, we analysis and create general theoretical model of video segmentation.
本文介绍了实现视频分割和场景聚类的算法。
This paper introduced algorithms for video segmentation and scene clustering.
提出一种基于背景模型的自动视频分割方法。
An automatic video segmentation algorithm based on background model is proposed in this paper.
视频分割是多媒体数据检索的关键技术之一。
Video segmentation is the key technique in multimedia data retrieval.
试着考虑将这些长视频分割成分开的短视频。
Consider segmenting a long video into separate, shorter videos.
本文提出了一种基于运动窗生成的时空视频分割方法。
This paper presents a spatiotemporal video segmentation algorithm based on the generation of motion window.
第三章介绍利用视频分割与粒子滤波实现多目标跟踪的算法。
In chapter three, we adopt the technique of video segmentation and particle filter to develop a new algorithm of multi-target tracking.
针对固定场景的视频，提出一种基于时空分割的视频分割算法。
This paper proposes an algorithm of video segmentation based on spatiotemporal information for the videos with fixed scenes.
在这两种情况下，高效精确的视频分割和视图合成都是关键问题。
In both situations, efficient and accurate video segmentation and view synthesis are key issues.
视频分割是视频结构化和检索的重要技术，目前主要通过镜头分割得到。
Video segmentation is an important technology for video structuring and retrieval, with most research work now focused on shot detection.
针对背景静止的立体视频提出了一种快速的基于背景重构的视频分割算法。
A fast video segmentation algorithm based on background reconstruction is proposed for stereo video with static backgrounds.
本文的主要研究贡献有：1。提出基于CNN差分图象合并的视频分割算法。
The main novel contributions of this paper are as follow: 1. Difference merged image algorithm based on CNN is presented.
针对背景相对运动是视频序列，提出了基于光流场阈值的CNN视频分割算法。
Aim at video sequences with dynamic background, the CNN video motion segmentation algorithm based on optical flow field threshold is presented.
论文中视频处理主要包括视频分割、视频合成、图像序列合成、格式转换等功能。
The video processing includes video segmentation, video synthesis, video compose, video format conversion and other functions.
用这种方法对一帧图像进行一次运动估计约需0.1秒，可以用于实时的视频分割系统中。
It only takes 0.1s for the algorithm to perform motion estimation for a frame of picture. It can be used in real time video segmentation system.
为了实现基于内容的视频检索和恢复，必须将视频分割成不同的镜头，再对镜头进行描述。
To realize the content-based retrieval and recovery of video, video segmentation into shots and its description are necessary.
近年来在视频分割领域出现了大量算法，这些算法总体上来说可以分为压缩域和非压缩域两类。
A large number of video segmentation techniques have been developed in recent years. All of them can be classified (into) two groups, uncompressed field and compressed field.
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。
This paper introduces video segmentation of temporal segmentation and spatial segmentation, presents a video object segmentation algorithm based on multi-frames difference.
实现数字图书馆中基于内容的视频检索的关键技术包括：视频数据建模、视频分割、视频索引、视频查询与浏览。
Key techniques for the realization of content-based video retrieval in digital library include video data modeling, video segmentation, video indexing, and video searching and browsing.
针对背景相对静止的视频序列，提出了基于CNN差分图象合并的视频分割算法，并构建了与该算法相关的五个CNN模板。
Aim at video sequences with static background, the difference merged image algorithm based on CNN is presented. In order to realize the algorithm, five CNN templates are constructed.
此外，针对背景静止的特殊情况，本文介绍了一种通过背景估计进行视频分割的思路，并且提出了一种自适应的背景估计方法。
In addition, for the special case of stationary background, this paper introduces an idea of video segmentation through background estimating and proposes an adaptive method for background estimating.
实验结果表明，此算法可以达到精确的分割效果，且视频分割后可以显著减小立体视频匹配时间，进而减少数据传输量和存储空间。
The result of the experiments shows that it can achieve the segmentation accurately and reduce the stereo matching time and the information storage significantly.
目前在美国和欧洲的一些权威研究机构，正在致力于采用CNN对视频分割进行研究，这是一种较新的方法，是神经网络在应用方面的前沿领域。
Now some American and European research government are taking up with the video segmentation based on CNN, which is new way and the foreland of neural network application field.
目前在美国和欧洲的一些权威研究机构，正在致力于采用CNN对视频分割进行研究，这是一种较新的方法，是神经网络在应用方面的前沿领域。
Now some American and European research government are taking up with the video segmentation based on CNN, which is new way and the foreland of neural network application field.
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