基于非局部3D残差网络的视频指纹算法

为了实现视频拷贝的快速准确检索,提出一种基于非局部3D残差网络的紧凑视频指纹。该算法以三胞胎网络架构为基础,采用非局部模块3D残差网络同时捕获视频的全局与局部时空信息,在特征提取部分末端加入量化编码层,实现了原始视频数据到离散指纹码的端到端映射;设计了由角度关系三元组损失和量化误差损失组成的网络目标函数。大量的实验结果表明,与对比算法相比,该算法在保持紧凑的同时鲁棒性与独特性均表现突出,查准率与查全率有明显提升。     

基于双流网络融合与时空卷积的人体行为识别

针对视频中存在噪音,无法更好地获取特征信息,造成动作识别不精准的问题.提出了一种基于时空卷积神经网络的人体行为识别网络.将长时段视频进行分段处理,分别把RGB图片和计算出的光流图输入到两个卷积神经网络(CNN)中,使用权重相加的融合算法将提取的时域特征和空域特征融合成时空特征.形成的中层语义信息输入到R(2+1)D的卷积中,利用ResNet提高网络性能,最后在softmax层进行行行为识别.在UCF-101和HMDB-51数据集上进行实验,获得了92.1％和66.1％的准确率.实验表明,提出的双流融合与时空卷积网络模型有助于视频行为识别的准确率提高.     

深度度量学习视频指纹算法

在满足鲁棒性、独特性前提下,为了提高视频指纹系统紧凑性,提出一种端到端的深度度量学习视频指纹算法.网络整体框架由权值共享的三分支网络组成,分支网络采用改进的3D残差网络将多层特征融合并进行压缩,实现视频数据到指纹的端到端映射.网络目标函数由度量和分类双损失函数组成,其中,设计的边界约束三元组角度度量损失函数克服了普通三元组损失函数对特征相关性表达不足的问题;分类损失函数弥补了度量损失对样本特征整体分布不敏感的问题.在公开数据集FCVID上对文中算法、传统方法和深度方法进行了大量实验.结果表明,深度度量学习视频指纹算法在鲁棒性、独特性提高的同时紧凑性显著提高.     

基于多模态特征学习的人体行为识别方法

由于从单一行为模态中获取的特征难以准确地表达复杂的人体动作,本文提出基于多模态特征学习的人体行为识别算法.首先采用两条通道分别提取行为视频的RGB特征和3D骨骼特征,第1条通道C3DP-LA网络由两部分组成:(1)包含时空金字塔池化(Spatial Temporal Pyramid Pooling,STPP)的改进3D CNN;(2)基于时空注意力机制的LSTM,第2条通道为时空图卷积网络(ST-GCN),然后,本文将提取到的两种特征融合使其优势互补,最后用Softmax分类器对融合特征进行分类,并在公开数据集UCF101和NTU RGB+D上验证.实验表明,本文提出的方法与现有行为识别算法相比具有较高的识别准确度.     

(2+1)D多时空信息融合模型及在行为识别的应用

针对常规的卷积神经网络时空感受野尺度单一,难以提取视频中多变的时空信息的问题,利用（2+1）D模型将时间信息和空间信息在一定程度上解耦的特性,提出了（2+1）D多时空信息融合的卷积残差神经网络,并用于人体行为识别.该模型以3×3空间感受野为主,1×1空间感受野为辅,与3种不同时域感受野交叉组合构建了6种不同尺度的时空感受野.提出的多时空感受野融合模型能够同时获取不同尺度的时空信息,提取更丰富的人体行为特征,因此能够更有效识别不同时间周期、不同动作幅度的人体行为.另外提出了一种视频时序扩充方法,该方法能够同时在空间信息和时间序列扩充视频数据集,丰富训练样本.提出的方法在公共视频人体行为数据集UCF101和HMDB51上子视频的识别率超过或接近最新的视频行为识别方法.     

融合非局部神经网络的行为检测模型

针对在视频行为检测中卷积神经网络(CNN)对时域信息理解能力不足的问题,提出了一种融合非局部神经网络的行为检测模型.模型采用一种双分支的CNN结构,分别提取视频的空间特征和运动特征.将视频单帧和视频连续帧序列作为网络输入,空间网络对视频当前帧进行2D CNN特征提取,时空网络采用融合非局部模块的3D CNN来捕获视频帧...     

基于边缘映射抽样与多维尺度压缩的紧凑图像哈希算法

为了充分利用图像的显著特征来生成鲁棒哈希,并提高其生成效率,提出基于边缘映射抽样与多维尺度压缩的紧凑图像哈希算法。联合双线性插值与高斯低通滤波,对初始图像进行预处理操作;引入双阈值Canny算子,提取预处理图像的显著边缘映射。将预处理图像与边缘映射分割为一系列的非重叠子块;计算每个子块的像素值总和,找出其中元素值最大的子块,从中进行选择性抽样,确定对应的包含结构信息最丰富的子块。基于SVD(Singular Value Decompostion)机制,将子块的奇异值视为鲁棒特征;利用Fourier变换与残差机制,提取预处理图像的全局显著特征。将两种特征组合,形成中间哈希序列,再引入多维尺度机制压缩获取紧凑哈希,并通过Logistic映射来实施加密,形成最终的哈希。测试数据表明:较已有的哈希方案而言,该方案具备更高的鲁棒性与哈希生成效率,呈现出更好的ROC曲线。     

基于视频残差神经网络的深度步态识别

步态识别是根据人体的行走方式进行身份识别. 目前, 大多数步态识别方法通过浅层神经网络进行特征提取, 在室内步态数据集表现良好, 然而在近年新公布的室外步态数据集中性能表现不佳. 为了解决室外步态数据集带来的严峻挑战, 提出了一种基于视频残差神经网络的深度步态识别模型. 在特征提取阶段, 基于提出的视频残差块构建深层3D卷积神经网络(3D CNN), 提取整个步态序列的时空动力学特征; 然后, 引入时序池化和水平金字塔映射降低采样特征分辨率并提取局部步态特征; 使用联合损失函数驱动训练过程, 最后通过BNNeck平衡损失函数并调整特征空间. 实验分别在公开的室内 (CASIA-B)、室外(GREW、Gait3D)这3个步态数据集上进行. 实验结果表明, 该模型在室外步态数据集中的准确率以及收敛速度优于其他模型.     

一种鲁棒的视频指纹提取和匹配方法

在视频广告监测的解决方案中,视频指纹日益受到重视。针对目前视频指纹鲁棒性不强、匹配方法效率不高或适用度不广的缺点,提出一种快速鲁棒的视频指纹算法:在视频指纹提取阶段,结合改进的Harris检测和改进的帧间差异法分别提取图像特征、运动特征以产生鲁棒的视频指纹;在指纹匹配阶段,设计了一种分层方案,该方案结合了典型匹配算法适用度广、位置敏感哈希(LSH)高效性的优点。实验表明,在亮度变换和尺寸变化以及常见的噪声攻击中,提取的视频指纹有较强的鲁棒性,提取、匹配的效率高。该方法满足视频监测准确性及实时性的要求。     

基于时间注意力机制和EfficientNet的视频暴力行为检测

针对一般的暴力行为检测方法模型参数量大、计算复杂度高、准确率较低等问题,提出一种基于时间注意力机制和EfficientNet的视频暴力行为检测方法。首先将通过对数据集进行预处理计算得到的前景图输入到网络模型中提取视频特征,同时利用轻量化EfficientNet提取前景图中的帧级空间暴力特征,并利用卷积长短时记忆网络（ConvLSTM）进一步提取视频序列的全局时空特征;接着,结合时间注意力机制,计算得到视频级特征表示;最后将视频级特征表示映射到分类空间,并利用Softmax分类器进行视频暴力行为分类并输出检测结果,实现视频的暴力行为检测。实验结果表明,该方法能够减少模型参数量,降低计算复杂度,在有限的资源下提高暴力行为检测准确率,提升模型的综合性能。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.