π演算的Petri网语义研究

为弥补π演算的固有缺陷．提出一种将π演算映射为Petri网语义的方法．该方法将π演算分为基本单元、顺序、并发、选择和递归等几种基本结构．分别映射为Petri网．然后复合构成复杂的系统．π演算的Petri网语义可形象地描述系统的物理结构和动态行为，可直接从模型网络结构上定性分析系统的性质．最后,利用该方法将移动汽车网络的π演算模型映射为Petri网，验证了方法的有效性．     

一种通用的基于基本语义单元的体育视频内容分析框架

体育视频内容分析的研究现状集中在语义标注，对体育视频的句法分段和框架的研究较少．本文在分析了体育视频基本特征的基础上，提出了体育视频中基本语义单元(Basic Semantic Unit，简称BSU单元)的概念；继而提出了一种基于BSU的体育视频内容分析的通用框架；并且以足球视频为例，实例化了这种通用的体育视频内容分析框架．初步的实验结果表明，这种基于BSU的体育视频内容分析框架是有效和可行的．     

基于Petri网推理的监控视频事件识别

Petri网模型对复合时序事件具有较好的描述和检测能力,提出基于Petri网描述和推理的监控视频事件信息的自动识别方法。将运动目标及其特征间的空间关系用Petri网的库表示,时间关系及其他推理规则用变迁表示,结合低级计算机视觉算法获得的运动目标特征以及基本事件信息,通过对Petri网推理执行,实现监控视频复杂语义事件的交互式查询,通过实验验证了该方法的有效性。     

XML查询方法的设计与研究

提出了用Petri网建立XML语义结构模型的设计方法，根据XML的DTD结构建立描述XML语义结构的Petri网模型，DTD结构中的元素声明和属性声明对应Petri网模型中的一个或一组变迁，声明中的元素或属性对应Petri 网模型中的库所，把XML查询中的路径表达式定义为Petri网库所中的Token。根据Petri网模型的结构生成存储XML数据的关系数据库模型，将XML的查询问题最终转化为数据库中数据的查询操作。 XML；Petri网；路径表达式；查询     

基于一种新型混杂Petri网的混杂系统建模与控制

为了增强混杂Petri网模型描述能力和克服一阶混杂Petri网模型语义错误的问题，定义一种新的混杂Petri网模型——广义混杂Petri网，提出了相应的迁移使能和迁移引发语义．通过划分标识等价类提出了广义混杂Petri网连续迁移瞬时引发速率的有效求解方法和模型行为演变分析方法．给出了广义混杂Petri网模型的混杂系统建模实例，同时结合模型行为演变和线性规划实现了混杂系统的优化控制．研究结果表明，所定义的模型描述能力更强，模型语义正确合理．能够有效描述和分析混杂系统．     

UML2.0顺序图的形式化研究

在UML2.0规范中顺序图的语义仍然是以自然语言的形式描述的,为实现对顺序图的自动化分析和验证,必须为顺序图定义一种形式化的语义模型.为此首先给出了UML顺序图的一种符合BNF范式的形式化语法,然后为该语法中的非终止符定义转换规则,将UML顺序图中的基本动作转换为加标Petri网组件,最后定义了各种合成操作,利用这些合成操作可以将UML顺序图的加标Petri网组件转换为加标Petni网.     

基于Petri网的工作流模型

Petri网是一种很有效的模型描述语言，不仅能描述系统的结构特性，同时还能描述其动态特性，尤其适用于描述含有并行成分的系统，而工作流技术作为实现企业过程集成的有效手段，广泛用于各种场合。在简要介绍了Petri网和工作流基本知识的基础上，指出了基于Petri网的工作流模型的优点，讨论了将工作流模型转化为Petri网模型的基本原则，并且提出了一种扩展的Petri网模型作为一种工作流建模的工具，最后对基于Petri网的工作流模型进行了总结。     

基于有色Petri网的Web服务合成建模

分析了用一般Petri网进行Web服务合成建模的不足,提出用有色Petri网进行改进.利用有色Petri网原理给出了Web服务的形式化定义,描述了Web服务合成的五种基本结构的Petri网模型,通过一个行程规划实例,分析了行程中的不同Web服务,描述了行程规划服务由不同Web服务合成的有色Petri网模型,展示了有色Petri网在Web服务合成建模中的优势.     

基于Agent的对象有色Petri Net及其在制造单元建模中的应用

在对象有色Petri网的基础上，引入知识库、目标库和规划模块，提出一种基于Agent的对象有色Petri网模型。以一个制造单元为例，介绍了使用该模型用于Agent系统建模的方法，以及Agent之间异步消息通信机制。最后，通过分析了由基于Agent的对象有色Petri网简化得到的制造单元的普通Petri网模型，验证了模型具有的三级活性、并发、有效性三个特征。     

基于Petri网合成与化简的分布式数据库系统并发控制的死锁检测

利用模块化设计的思想，首先为分布式数据库系统中各事务的每一种操作(读锁、写锁、解锁)构造一个基本的加权Petri网模型，并给出了加权Petri网共享合成的概念、然后利用共享合成技术，动态地构造各站点的加权Petri网模型，以适应系统的动态变化．此外，本文利用Petri网的化简技术，极大地简化了各站点的Petri网模型，之后利用同步合成技术构造出整个系统的Petri网模型，较好地解决了Petri网的可达性分析中可能出现的状态“爆炸”问题．最后给出了判断整个系统是否出现死锁的充分必要条件．     

Common Visual Cues for Sports Highlights Modeling

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. The proposed approach exploits the typical structure of a wide class of sports videos, namely those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For these sports, a modeling scheme based on a limited set of visual cues and on finite state machines that encode the temporal evolution of highlights is presented, that is of general applicability to this class of sports. Visual cues encode position and speed information coming from the camera and from the object/athletes that are present in the scene, and are estimated automatically from the video stream. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.     

Using Webcast Text for Semantic Event Detection in Broadcast Sports Video

Sports video semantic event detection is essential for sports video summarization and retrieval. Extensive research efforts have been devoted to this area in recent years. However, the existing sports video event detection approaches heavily rely on either video content itself, which face the difficulty of high-level semantic information extraction from video content using computer vision and image processing techniques, or manually generated video ontology, which is domain specific and difficult to be automatically aligned with the video content. In this paper, we present a novel approach for sports video semantic event detection based on analysis and alignment of webcast text and broadcast video. Webcast text is a text broadcast channel for sports game which is co-produced with the broadcast video and is easily obtained from the web. We first analyze webcast text to cluster and detect text events in an unsupervised way using probabilistic latent semantic analysis (pLSA). Based on the detected text event and video structure analysis, we employ a conditional random field model (CRFM) to align text event and video event by detecting event moment and event boundary in the video. Incorporation of webcast text into sports video analysis significantly facilitates sports video semantic event detection. We conducted experiments on 33 hours of soccer and basketball games for webcast analysis, broadcast video analysis and text/video semantic alignment. The results are encouraging and compared with the manually labeled ground truth.      

Highlights modeling and detection in sports videos

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. This application is particularly interesting for broadcasters, since they extensively use manual annotation to select interesting highlights that are edited to create new programmes. The proposed approach exploits the typical structure of a wide class of sports videos, namely, those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For this class of sports, a modeling scheme based on a limited set of visual cues and on finite state machines (FSM) that encode the temporal evolution of highlights is presented. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.     

Bayesian belief network based broadcast sports video indexing

This paper presents a probabilistic Bayesian belief network (BBN) method for automatic indexing of excitement clips of sports video sequences. The excitement clips from sports video sequences are extracted using audio features. The excitement clips are comprised of multiple subclips corresponding to the events such as replay, field-view, close-ups of players, close-ups of referees/umpires, spectators, players’ gathering. The events are detected and classified using a hierarchical classification scheme. The BBN based on observed events is used to assign semantic concept-labels to the excitement clips, such as goals, saves, and card in soccer video, wicket and hit in cricket video sequences. The BBN based indexing results are compared with our previously proposed event-association based approach and found BBN is better than the event-association based approach. The proposed scheme provides a generalizable method for linking low-level video features with high-level semantic concepts. The generic nature of the proposed approach in the sports domain is validated by demonstrating successful indexing of soccer and cricket video excitement clips. The proposed scheme offers a general approach to the automatic tagging of large scale multimedia content with rich semantics. The collection of labeled excitement clips provide a video summary for highlight browsing, video skimming, indexing and retrieval.     

基于内容的视频查询系统研究

由于多媒体数据库管理和检索的效率直接决定了人们利用多媒体数据信息的效率,因此随着MPEG-7标准的提出,基于内容的图象/视频存储和检索已成为研究的热点.为了快速地对视频进行浏览和检索,在研究基于内容的视频数据库管理和检索等热点问题的基础上,首先使用MPEG-7视觉内容描述子和语义描述子来构建视频数据库的语义结构,并结合底层视觉特征和高层语义特征,采用相关反馈机制和半自动权重更新体制来对视频数据库进行管理和检索;然后采用语法分析器来支持自然语言查询;最后在此基础上实现了基于内容的视频数据库的管理和查询系统.实验证明,该系统能够有效地对视频数据进行管理和检索,并且具有一定的智能性和适应性.     

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。     

基于视觉注意模型和HMM的足球视频语义分析

HMM模型具有良好的适应性,可以自动学习,对预测随机时序数据性能良好。场景是足球视频的基本特征,场景的转换体现了足球视频的摄制、编辑模式,表现了足球视频的语义。提出了一种基于场景分析和HMM的视频语义分析框架,用于识别足球视频中的一些语义事件。为了克服以往基于主颜色和其他底层特征的视频场景分析中存在的较大误差,又提出基于视觉注意模型对足球视频中的场景进行分析。实验结果表明,基于场景分析和HMM的事件识别方法对足球视频中的任意球事件有良好的识别效果     

ANALYSIS OF VISION BASED SYSTEMS TO DETECT REAL TIME GOAL EVENTS IN SOCCER VIDEOS

The purpose of this article is to analyze current vision-based systems from a soccer video semantic point of view such as video summarization, features analysis, and provision of augmented information. Currently, computer vision techniques are applicable in a challenging soccer context. Scene interpretation is performed based on the complexity of the semantic. For each area of vision-based systems, computer vision methodologies are analyzed along with their strengths and weaknesses. We have also investigated whether the existing approaches are equally applicable for real-time soccer video semantic analysis.     

多模态体育视频语义分析

以足球运动为例提出了一种体育视频语义结构，并提出相应的语义分析框架。视频被分解为纯视频流和音频流两种模态，每种模态均可依次提取和综合出低层内容和中层内容。视频流可根据低层（物理）内容分割为物理镜头，然后根据特定的中间层内容可以确定为语法镜头。音频也可以在物理特征的基础上形成有意义的中间层内容，如解说员兴奋时的声音。最后，根据视频流和音频流的中间层内容，按照足球比赛转播的规律，分析出比赛中的精彩事件，并选取相关的镜头作为反映此事件的序列组合。