Transductive multi-distance learning for video search

Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of labeled training data in many real-world application areas, such as video annotation. As a significant factor of these algorithms, however, pair-wise similarity metric of samples has not been fully investigated. Specifically, for existing approaches, the estimation of pair-wise similarity between two samples relies on the spatial property of video data. On the other hand, temporal property, an essential characteristic of video data, is not embedded into the pair-wise similarity measure. Accordingly, in this paper, a novel framework for video annotation, called Joint Spatio-Temporal Correlation Learning (JSTCL) is proposed. This framework is characterized by simultaneously taking into account both the spatial and temporal property of video data to improve the estimation of pair-wise similarity. We apply the proposed framework to video annotation and report superior performance compared to key existing approaches over the benchmark TRECVID data set.     

Concurrence for infinite-dimensional quantum systems

Concurrence is an important entanglement measure for states in finite-dimensional quantum systems that was explored intensively in the last decade. In this paper, we extend the concept of concurrence to infinite-dimensional bipartite systems and show that it is continuous and does not increase under local operation and classical communication.     

一种复杂背景下双层框架背景的建模方法

提出了一种经过改进的,适用于动态变化环境的背景建模和前景检测方法。该方法把背景分为动态层和静态层。利用能量描述子来分析动态层中的运动状态。同时,使用网格滤波器来减弱由于光照突变造成的影响。实验结果显示,对于包含光照突变和复杂背景的视频序列,该方法与3种经典的算法相比,有着更好的效果。     

多场景视频监控中的人物连续跟踪

针对多场景监控,提出一种能在非重叠的多摄像机之间,实时地检测与被跟踪行人（目标）的视频监控系统。该系统分别对每个摄像机视频进行背景建模、前景检测以及运动目标的特征提取,当目标离开摄像机视域的时候,根据已知的拓扑关系向相关摄像机发布监控任务,当有目标进入处于有效监控状态的摄像机视域时,进行目标匹配,从而实现在多摄像机系统中对行人目标的持续跟踪。实验表明,该系统在非重叠摄像机场景中能实现实时的、鲁棒的目标跟踪。     

A new calibration model of camera lens distortion

Lens distortion is one of the main factors affecting camera calibration. In this paper, a new model of camera lens distortion is presented, according to which lens distortion is governed by the coefficients of radial distortion and a transform from ideal image plane to real sensor array plane. The transform is determined by two angular parameters describing the pose of the real sensor array plane with respect to the ideal image plane and two linear parameters locating the real sensor array with respect to the optical axis. Experiments show that the new model has about the same correcting effect upon lens distortion as the conventional model including all the radial distortion, decentering distortion and prism distortion. Compared with the conventional model, the new model has fewer parameters to be calibrated and more explicit physical meaning.     

基于声纹特征和集成学习的变压器缺陷诊断方法

变压器运行过程中产生的振动噪声与其运行状态及内部缺陷情况直接相关,对其声纹信号开展特征分析,有助于进一步了解设备运行工况,保障电力系统安全稳定运行。文中以声纹特征分析为基础,兼顾诊断效率与准确性,提出一种基于卷积神经网络及集成学习模型的变压器缺陷诊断方法。该方法以变压器声纹数据的时域及频域信号为多通道输入混合特征,构建了基于卷积神经网络模型和声纹特征分析法的集成学习模型,可实现变压器声纹特征的有效识别,并通过由多个基学习器组成的集成学习模型提高了变压器缺陷诊断的准确性。基于文中所构建的变压器声纹样本库,可得到该方法对变压器单一缺陷的识别准确率为99.2%,对变压器混合缺陷的识别准确率为99.7%。研究结果表明该方法可有效识别变压器的运行状态,为变压器运维检修提供技术参考。     

Applications of projected circle centers in camera calibration

A novel camera calibration algorithm for solving the problems of both circles based and spheres based camera calibration is proposed. By treating the images of both a circle and a sphere as a revolving stick, the introduced algorithm gives the constraint of the imaged absolute conic (IAC) with the help of the projected circle centers. It is also introduced on how to compute the projected circle centers of different calibration objects. Once the projected circle centers are computed, the Euclidean structure then can be determined by the constraint of the IAC. Experiments with simulated and real data are carried out to show the validity of the proposed camera calibration algorithm.     

Online learning of dynamic multi-view gallery for person Re-identification

Person re-identification receives increasing attentions in computer vision due to its potential applications in video surveillance. In order to alleviate wrong matches caused by misalignment or missing features among cameras, we propose to learn a multi-view gallery of frequently appearing objects in a relatively closed environment. The gallery contains appearance models of these objects from different cameras and viewpoints. The strength of the learned appearance models lies in that they are invariant to viewpoint and illumination changes. To automatically estimate the number of frequently appearing objects in the environment and update their appearance models online, we propose a dynamic gallery learning algorithm. We specifically build up two datasets to validate the effectiveness of our approach in realistic scenarios. Comparisons with benchmark methods demonstrate promising performance in accuracy and efficiency of re-identification.     

互动社会语言学与交际民族志学的渊源探究

本文从核心概念、研究方法、研究对象等方面对互动社会语言学和交际民族志学进行了梳理。互动社会语言学中的"语境提示"是交际民族志学中核心概念的动态、深层次发展;互动社会语言学沿袭了交际民族志学实地考察、深度参与的研究方法;互动社会语言学的落脚点是会话策略,这是对交际民族志学所研究的单一、静态的言语事件的动态汇总。经过比较分析,揭示了互动社会语言学继承和发展了交际民族志学的精髓。