EAST等离子体剖面数据可视化

EAST已经初步建立较为完善的分析数据数据库,相较于存储原始采集信号的一级数据库,科研人员称这个包含EAST各个子系统（诊断、加热、加料等）具体物理信息的数据库为EAST二级数据库.为了便于科研人员从EAST二级库中查看和分析等离子体剖面数据,采用Python及PyQt开发了数据可视化图形用户软件.它可以分析比较来自不同诊断系统的剖面数据,具有将剖面数据用图形表示出来并改变图形属性等功能.同时在等离子体放电实验期间,提供了炮间分析等离子体剖面数据可视化的功能.本文介绍了EAST剖面数据可视化系统的设计方案与实现方法,同时也给出了系统的初步测试结果.     

视频点播系统中的仿真与性能研究

介绍了视频点播系统的仿真实现技术，通过仿真器对视频点播系统中的节目调度方案进行了仿真测试与分析。分析测量结果表明，采用新的调度方案的视频点播系统能够比传统的视频点播系统提供更多的用户点播并发数，提高了视频服务器的性能。     

视频数据的存储调度优化策略

文章论述了采用多个代理服务器的视频点播系统中负载平衡的基本原理,指出了现有动态均衡解决方案存在的问题,各节点机的负载评价问题和任务迁移的抖动问题,进一步分析了负载接入量和接入许可问题,以及文件存储的静态调整技术.同时提出了以节目点播概率为参数,周期性调整系统中的节目文件存储,以便使用户的点播请求合理分配到各视频服务器,从而实现整个系统的负载均衡的基于点播率的存储动态调整算法.     

一种视频点播日志分析系统的设计与实现

视频点播系统产生大量日志信息,包括机顶盒注册、页面浏览、会话和全局会话负载均衡等日志信息。通过采集这些日志,可以分析出用户的点播流程和视频点播系统的运行状态等信息。对一种视频点播日志分析系统的体系结构进行分析,设计基于C/S架构的日志采集模块和基于多线程并行处理方式的日志分析模块,实现一个视频点播日志分析系统。仿真实验证明,多线程并行处理方式比单线程处理在日志分析的效率上明显提高。     

校园网格中VOD系统动态负载均衡算法研究

校园网格中的视频点播系统表现形式的直观性,自主性一直受到校园内师生的广泛关注,但视频服务器节点响应能力低下,视频点播中的缓冲和延迟而导致点播不能适应实时、连续稳定的视频流。通过分析视频点播系统面临的诸多问题,根据校园网格的特点和影响负载均衡的因素,对基于校园网格的负载均衡技术进行了分析,提出一种动态负载均衡算法,实现校园网络中视频资源的共享、均衡校园视频资源的负载。测试表明该算法可行、有效。     

.一种基于传输流的视频点播VCR功能实现方法*

传输流（TS）作为多媒体数据传输和存储格式在网络视频点播中得到了越来越广泛的应用,但其面向广播设计的固有的封装格式使其在点播系统中难以进行灵活的人机交互操作（VCR功能）。针对TS文件结构的特点,设计了TS文件的时间快速定位算法,并针对TS文件的快进、快退过程提出了高效的数据挑选机制,保证了基于TS文件的视频点播人机交互VCR功能的实现。     

基于局域网的无数据库教学视频节目点播系统

在视频点播系统的构建中,人们往往注重于系统功能的全面性,而忽视了系统的易维护性和简单实用性。利用ASP脚本对象FileSystemObject的文件管理功能,构建了一个不需要数据库管理的教学视频点播系统,提供了构建简单实用的教学视频节目点播系统的另一种思路。     

3TNet视频点播系统中用户点播行为仿真及应用

通过分析3TNet视频点播（VOD）系统中的媒体服务情况,集中研究了用户点播模式、媒体热度分布,根据实际系统中用户点播分布的研究结果构建了一个用户点播行为的仿真平台。该平台描述了用户点播行为的一般数学模型,刻画了用户进入系统的行为分布特点。实验表明,VOD用户点播行为仿真平台能够客观真实地反映用户的点播行为,并通过构建一个仿真的视频点播系统来验证此平台的可用性,利用此仿真视频点播系统比较了两种部署策略的优劣。     

EAST实验视频采集系统的设计与实现

在全超导非圆截面核聚变实验装置EAST中,为了实时监控等离子体状态,采用高速摄像头配合1394视频采集卡的视频采集系统,实现等离子放电视频的实时显示与采集,同时采集的视频文件用于网络发布.     

一种依赖社会网络的P2P视频点播推荐机制

提出一种新颖实用的视频推荐机制,用于缓解高质量P2P视频点播服务中存在的节点带宽饥饿现象.通过分析中国科技大学校园视频点播系统10个月的点播日志,发现视频在视频关联网络有聚簇的趋势,社会网络的现象明显.基于视频的社会网络特性,利用P2P视频点播系统已有的gossip协议,设计了一种新颖实用的基于社会网络的分布式视频推荐机制.利用gossip协议和视频之间的社会网络关系属性,此机制能够分布式的并且低代价的给饥饿节点推荐最优的视频.饥饿的节点通过接受推荐来避免继续陷入饥饿.实验表明该推荐机制能有效地缓解点播节点的带宽饥饿,提高P2P网络资源的利用效率.     

Delivery of compressed videos from video servers employing cycle-based data-block retrieval discipline

The significant frame size variability exhibited in the compressed videos imposes a great challenge on network delivery. In this paper we propose an efficient flow control scheme, employed in the peer stations (i.e., servers and clients), for delivery of prestored compressed videos in a video-on-demand (VOD) system. This scheme resorts to an off-line analysis on the video frame sizes and server properties for figuring out the necessary buffer space and network bandwidth. The server platform of particular interest obeys a cycle-based data-block retrieval discipline, which is an essential technique to reduce the disk seek time for leveraging the disk throughput for supporting a large number of concurrent video accesses. Such a discipline is taken into account here to guarantee smooth delivery of variable-bit-rate videos. In run-time a server-driven control model is in use, where a server performs the primary flow control task, without relying on any feedback from clients. The scheme has been implemented in our prototype VOD system to support both unicast- and multicast communication paradigms under an RSVP-enabled network.     

Novel Shot Boundary Detection in News Streams Based on Fuzzy Petri Nets

ABSTRACT

With the advent of a digital era, people have encountered some difficulty in using and absorbing overwhelming information generated by technological advances in multimedia. Thus, the development of video summarization enables people to catch a general idea about videos in a short time. In this paper, we focus on the shot change, a part of the video summarization, to conduct an experimental sample on news programs. Moreover, a high-level fuzzy Petri net model is presented to describe the frame combination which indicates a shot boundary used for a video frame sequence in order to detect both cut transitions and gradual transitions. This study has used feature functions to estimate the direct shot change in consideration of video shot boundary detection which adopts the HLFPN model to find a threshold value. The experimental results manifest that the proposed system saves a lot of time and reduces the occurrence of improper shot changes caused by the motions of objects and cameras when comparing the proposed approach with other existing ones.     

Movie Scene Recognition Using Panoramic Frame and Representative Feature Patches简

Recognizing scene information in images or has attracted much attention in computer vision or videos, such as locating the objects and answering ＂Where am research field. Many existing scene recognition methods focus on static images, and cannot achieve satisfactory results on videos which contain more complex scenes features than images. In this paper, we propose a robust movie scene recognition approach based on panoramic frame and representative feature patch. More specifically, the movie is first efficiently segmented into video shots and scenes. Secondly, we introduce a novel key-frame extraction method using panoramic frame and also a local feature extraction process is applied to get the representative feature patches （RFPs） in each video shot. Thirdly, a Latent Dirichlet Allocation （LDA） based recognition model is trained to recognize the scene within each individual video scene clip. The correlations between video clips are considered to enhance the recognition performance. When our proposed approach is implemented to recognize the scene in realistic movies, the experimental results shows that it can achieve satisfactory performance.     

基于C3D的足球视频场景分类算法

足球视频整场比赛持续时间较长,许多视频内容并非广大观众的兴趣所在,因此足球视频场景分类成为了近几十年来研究界的一项重要课题,许多机器学习方法也被应用于这个课题上.本文提出的基于C3D （三维卷积神经网络）的足球视频场景分类算法,将三维卷积运用于足球视频领域,并通过实验验证了本文算法的可行性.本文实验的流程如下：首先,基于帧间差分法和徽标检测法检测法对足球视频场景切换进行检测,实现镜头分割.在此基础上,提取分割镜头的语义特征并将其进行标记,然后通过C3D对足球事件进行分类.本文将足球视频分为7类,分别为远镜头、中镜头、特写镜头、回放镜头、观众镜头、开场镜头及VAR （视频助理裁判）镜头.实验结果表明,该模型在足球视频数据集上的分类准确率为96%.     

一种改进的视频关键帧提取算法研究

视频镜头分割和关键帧提取是基于内容的视频检索的核心问题.提出了一种改进的关键帧提取算法,其为视频检索奠定了基础,镜头分割部分采用改进直方图方法及基于像素方法的综合方法.首先,通过结合直方图交集及非均匀分块加权的改进直方图方法,根据视频内容将视频分割为镜头;然后,利用基于像素的帧差法,对得到的检测镜头进行二次检测,优化检测结果;最后,在HSV颜色空间的基础上,计算每个镜头内每帧的图像熵,从而确定关键帧序列.实验结果表明,提出的改进算法所得到的关键帧结构紧凑且分布均匀.     

基于MPEG-7的视频语义检索系统

设计和实现一个支持语义的分布式视频检索系统:"语寻"。该系统利用一个改进的视频语义处理工具(该工具基于IBM VideoAnnEx标注工具,并增加镜头语义图标注和自然语言处理的功能)对视频进行语义分析和标注,生成包含语义信息的MPEG-7描述文件,然后对视频的MPEG-7描述文件建立分布式索引,并同时分布式存储视频文件;系统提供丰富的Web查询接口,包括关键字语义扩展查询,语义图查询以及自然语句查询,当用户提交语义查询意图后,便能够迅速地检索到感兴趣的视频和片段,并且可以浏览点播;整个系统采用分布式架构,具备良好的可扩展性,并能够支持海量视频信息的索引和检索。     

Storyboard-based accurate automatic summary video editing system

The recent popularity of smart mobile devices has led to a significant increase in the needs of multimedia services. Finding new more efficient methods for automatic classification and retrieval of a large number of multimedia files will significantly reduce manpower costs. However, most current video content analysis methods adopt low-level features to analyze video frame by frame, and need to improve high-level semantic analysis on a number of issues. Hence, this study presents a storyboard-based accurate automatic summary video editing system that uses storyboard information, such as character dialogue, narration, caption, background music and shot changes, to enable accurate video content retrieval and automatic render summary videos. The proposed system can be applied to the course video trailer and the commercial video trailer for quick preview video content or suitable viewing configuration for smart mobile devices. Consequently, the audience can quickly understand the whole video story and the video editors can substantially reduce the time taken to publish videos.     

Video tampering dataset development in temporal domain for video forgery authentication

Videos are tampered by the forgers to modify or remove their content for malicious purpose. Many video authentication algorithms are developed to detect this tampering. At present, very few standard and diversified tampered video dataset is publicly available for reliable verification and authentication of forensic algorithms. In this paper, we propose the development of total 210 videos for Temporal Domain Tampered Video Dataset (TDTVD) using Frame Deletion, Frame Duplication and Frame Insertion. Out of total 210 videos, 120 videos are developed based on Event/Object/Person (EOP) removal or modification and remaining 90 videos are created based on Smart Tampering (ST) or Multiple Tampering. 16 original videos from SULFA and 24 original videos from YouTube (VTD Dataset) are used to develop different tampered videos. EOP based videos include 40 videos for each tampering type of frame deletion, frame insertion and frame duplication. ST based tampered video contains multiple tampering in a single video. Multiple tampering is developed in three categories (1) 10-frames tampered (frame deletion, frame duplication or frame insertion) at 3-different locations (2) 20-frames tampered at 3- different locations and (3) 30-frames tampered at 3-different locations in the video. Proposed TDTVD dataset includes all temporal domain tampering and also includes multiple tampering videos. The resultant tampered videos have video length ranging from 6 s to 18 s with resolution 320X240 or 640X360 pixels. The database is comprised of static and dynamic videos with various activities, like traffic, sports, news, a ball rolling, airport, garden, highways, zoom in zoom out etc. This entire dataset is publicly accessible for researchers, and this will be especially valuable to test their algorithms on this vast dataset. The detailed ground truth information like tampering type, frames tampered, location of tampering is also given for each developed tampered video to support verifying tampering detection algorithms. The dataset is compared with state of the art and validated with two video tampering detection methods.

     

基于三步筛选的视频渐变镜头检测

针对视频中的叠化与淡入淡出现象,提出一种基于三步筛选的渐变镜头检测算法。提取视频帧的亮度和方差作为特征,通过有限状态机实现初始渐变检测,并计算视频帧的颜色、共生矩阵、运动特征,从而进行三步筛选,保证检测的准确性。对TRECVID视频进行实验,结果表明,该算法对渐变具有较好的检测性能,对运动及闪光现象有较强的鲁棒性。     

一种结合内在动机理论的移动机器人环境认知模型

无人机视频由于拍摄的位置和场景不断移动,环境参数亦不断变化,采用以往针对固定场景的去雾方法不能达到最佳效果.为了使无人机视频去雾算法具有自适应性,提出一种基于滚动时域粒子群优化的视频去雾算法.将基于周期和事件混合驱动的滚动调度策略与粒子群算法(PSO)相结合,对可调去雾参数进行滚动优化调整,当与上次优化的帧间隔数大于阈值或环境和场景发生改变时,启动粒子群优化算法重新选取最佳去雾参数.针对无人机视频,分别应用所提出算法和固定去雾参数算法进行实验和对比分析,实验结果表明,对于环境因素动态变化的视频,所提出算法比固定去雾参数算法具有更好的对比度和视觉效果.