概率图模型

概率图模型及其图像与视频应用研究

Probabilistic Graphical Model and Its Application in Image and Video Information Processing

概率图模型在视频分割中的应用

Probabilistic Graphical Model and Its Application in Video Segmentation

基于概率图模型的科研文献主题演化研究

Study on Research Topic Evolution Based on Probabilistic Graphical Models

基于无向概率图模型的视频语义状态建模

Video Semantic State Modeling Using Undirected Probabilistic Graphic Models

基于概率图模型的人脸多特征跟踪

Probabilistic Graphical Model based Multiple Facial Feature Tracking

基于概率图模型技术的柱面全景图生成算法

Generating cylinder panorama based on probabilistic graphical models

一种新的面向对象的概率图模型

A New Object Oriented Probabilistic Graphic Model

关系马尔可夫网是一种能够有效处理复杂关系数据的判别式概率图模型，由马尔可夫网和关系模式结合产生。

Through the combination of probabilistic inference model and relational schema , it can deal with complicated relational data effectively .

基于激光扫描测距和视频图像处理的集卡定位方法概率图模型及其图像与视频应用研究

Container Alignment Based on Laser Measurement and Camera Calibration Probabilistic Graphical Model and Its Application in Image and Video Information Processing

先后提出了基于贝叶斯网络增强预测模型的跟踪方法和基于时空概率图模型的跟踪方法。

We propose a Bayesian network enhanced prediction based multiple facial feature tracking algorithm and a spatiotemporal graphical model based one .

条件随机场理论利用概率图模型表达空间邻域关系，并具有概率推理的优势。

Conditional Random Fields theory has unique advantages of spatial context analysis using probability graph model and probability inference in image segmentation .

马尔科夫逻辑网络是将一阶谓词逻辑与概率图模型相结合，以获取关系数据中的似然模型。

Markov Logic Networks combines First Order Logic and Probability Graphic Model in order to obtain model of likelihood in relational databases .

算法主要利用概率图模型中两个具体的模型：隐马尔可夫模型和马尔可夫随机场建立一个混合的概率图模型，对视频中背景建模。

A mixture model , which includes hidden Markov models and undirected probabilistic graphic models , is used to describe the dynamic background .

本文提出基于概率图模型的语义检索方法。该模型将查询关键词和异构对象分别映射到基于语义的概念层次，使得查询匹配在概念层次上进行。

We propose a retrieval method in a semantical-level based on probabilistic graphical model . The method uses hidden topics to describe query terms and objects respectively , thus matches queries and objects semantically .

应用澳大利亚新南威尔士东北地区汇水沉积物地球化学数据，采用分形求和法确定其分组界限，并与传统的概率图模型结果进行比较。

Population limits , derived from fractal modeling using a summation method , are compared with those derived from more conventional probability plot modeling of stream sediment geochemical data from north-eastern New South Wales .

概率图模型和变分推理是一个新型的机器学习框架，是处理不确定性和复杂性问题的有力工具，被广泛应用于计算机视觉和自然语言处理等领域。

Probabilistic graphical model and variational inference is a new machine learning framework , and is a good method applied to uncertain problem or complex problem . It is applied in computer vision and natural language processing widely .

概率图模型能很好处理不确定性，一阶逻辑可以简洁地表示知识，将概率与逻辑整合在同一个表示之中一直是人工智能领域的一个长期目标。

Probabilistic graphical models enable us to efficiently handle uncertainty . First-order logic enables us to compactly represent a wide variety of knowledge . Combining the both in a single representation has been a longstanding goal of AI research .

论文主要工作和研究成果总结如下：（1）结合概率图模型与面向对象建模方法的思想，提出了一种用于复杂问题中知识表示与推理的面向对象因子图模型。

The contributions and achievements of the thesis can be concluded as follows : ( 1 ) A new model named object-oriented factor graph is proposed for knowledge representation and reasoning , which combines the probabilistic models with the graph models .

其次，综合纹理分割的特点，将概率索引图模型和模糊K均值聚类算法结合起来，提出了基于概率索引图模型的纹理分割算法。

We further integrate texture segmentation based on the combination of the Probabilistic Index Maps model and a fuzzy k-means clustering algorithm . This thesis proposes the Probabilistic Index Maps model based texture segmentation algorithm .

复杂系统模糊概率故障图模型研究

Research on Fault Graph Model of Complex System Based on Fuzzy Probability

给出了模糊概率故障图模型的对象类定义、层次分解方法及模型特性。

The definition of object classes , layer decomposition method and characteristics of the model were depicted .

贝叶斯网络是一种表示多变量联合概率分布的图模型，它可以获得变量之间的条件独立关系。

A Bayesian network is a graphical model of joint multivariate probability distributions that captures properties of conditional independence between variables .

实际的生物学数据实验表明该方法性能优于单个神经网络，最近邻法和决策树。贝叶斯网络是一种表示多变量联合概率分布的图模型，它可以获得变量之间的条件独立关系。

Real biological data experiments have shown that this classification method outperformed than single neural networks , 1-nearest-neighbor classifiers and decision trees . A Bayesian network is a graphical model of joint multivariate probability distributions that captures properties of conditional independence between variables .

我们在本文里提出了一个新的数据表示模型&有向概率图，利用该模型无须比较两两文档即可非常容易地识别文档之间的共享短语；

We propose a new data model named DPG ( Directed Probability Graph ) to represent the documents , which makes it easy to recognize the shared phrases of them .

然后介绍了概率分布估计算法的核心&概率图模型；

Later the core of EDAs - probability map model was introduced and Bayesian network structure ( Bayesian Beliefs Networks ) was emphasized .

条件随机场是近年来提出的，用于标记和分割序列数据的条件概率模型，也是在给定输入节点条件下计算输出节点的条件概率的无向图模型。

Conditional Random Fields ( CRFs ), a recently introduced conditioned probabilistic model for labeling and segmenting sequential data , is a undirected graph model that calculate the conditional probability over output nodes given the input nodes .