基于改进全局—局部注意网络的室内场景识别方法

由于卷积神经网络(CNN)大多侧重于全局特征学习,忽略了包含更多细节的局部特征信息,使得室内场景识别的准确率难以提高。针对这一问题,提出了基于改进全局—局部注意网络(GLANet)的室内场景识别方法。首先,利用GLANet捕捉场景图像的全局特征和局部特征,增加图像特征中的细节信息;然后,在局部网络中引入non-local注意力模块,通过注意力图和特征图的卷积来进一步保留图像的细节特征,最后融合网络不同阶段的多种特征进行分类。通过在MIT Indoor67和SUN397数据集上的训练和验证,所提方法的识别准确率与LGN方法相比分别提高了1.98%和3.07%。实验结果表明,该算法能够有效捕获全局语义信息和精细的局部细节,显著提高了识别准确率。     

基于帧图像语义上下文的地形推理策略

基于场景帧图像语义上下文概念,提出一种融合不同场景时空信息的自适应在线地形推理策略TIBSC(Terrain Inference Based on Semantic Context).首先,提取不同场景图像近视场像素的特征向量和地形类别,构建地形样本候选数据库;然后,从地形样本候选数据库中选取与当前场景图像远视场区域语义上下文最相似的若干帧地形样本,构建当前场景地形判别数据库;最后,基于当前场景地形判别数据库及贝叶斯法则,对当前场景远视场区域像素所属地形类别进行推理.基于数据集的仿真实验表明:在影响TIBSC推理精度的所有因素中,基于语义距离准则的最优样本选择以及样本数据的在线扩充两大模型变量起主导作用.同时,基于相同分类特征的TIBSC策略在推理精度上优于同类研究结果.     

基于主题模型对象判别特征的场景识别

为了解决场景识别中存在的类内差异性与类间相似性问题,提出一种基于主题模型的对象判别特征的场景识别方法。首先,使用双卷积神经网络模型提取图像的全局空间特征和对象特征;然后用主题模型的方法对对象特征进行描述,将非欧几里得空间中的判别向量投影到欧几里得空间,得到对象判别图像描述符;最后将全局空间特征和对象判别图像描述符相融合,并采用分类器进行分类。实验结果表明,所提出的方法具有更好的场景分类性能。     

基于多特征融合和卷积神经网络的植物叶片识别

植物叶片识别是植物自动分类识别研究的重要分支和热点,利用卷积神经网络进行图像分类研究已成为主流.为了提高植物叶片识别准确率,提出了基于多特征融合和卷积神经网络的植物叶片图像识别方法.首先对植物叶片图像进行预处理,提取LBP特征和Gabor特征,将多特征相加融合输入网络进行训练,使用卷积神经网络(AlexNet)构架作为分类器,利用全连接层对植物叶片进行识别.为了避免过拟合现象,使用"dropout"方法训练卷积神经网络,通过调节学习率、dropout值、迭代次数优化模型.实验结果表明,基于多特征融合的卷积神经网络植物叶片识别方法对Flavia数据库32种叶片和MEW2014数据库189种叶片识别分类效果较好,平均正确识别率分别为93.25％和96.37％,相比一般的卷积神经网络识别方法,该方法可以提高植物叶片的识别准确率,鲁棒性更强.     

多特征和SVM相融合的三维物体识别方法

研究三维物体识别问题,摄像机从不同角度拍摄三维物体,获取的三维物体图像变化比较大,传统方法采用单一特征或简单多特征难以正确描述三维物体,导致三维物体识别的准确率较低.为了提高三维物体识别准确率,提出一种多特征和支持向量机相融合的三维物体识别方法.首先分别提取三维物体的颜色特征、纹理特征和不变矩特征,然后采用主成分分析消除各特征间的冗余信息,最后采用支持向量机建立三维物体识别模型.采用三维物体图像数据库COIL-100进行测试实验,结果表明,相对于传统识别方法,改进方法不仅提高了三维物体识别准确率,同时加快识别速度,为三维物体识别提供了一种新的识别方法.     

基于中层细微特征提取与多尺度特征融合细粒度图像识别

在细粒度视觉识别领域,由于高度近似的类别之间差异细微,图像细微特征的精确提取对识别的准确率有着至关重要的影响。现有的相关热点研究算法中使用注意力机制提取类别特征已经成为一种趋势,然而这些算法忽略了不明显但可区分的细微部分特征,并且孤立了对象不同判别性区域之间的特征关系。针对这些问题,提出了基于中层细微特征提取与多尺度特征融合的图像细粒度识别算法。首先,利用通道与位置信息融合中层特征的权重方差度量提取图像显著特征,之后通过通道平均池化获得掩码矩阵抑制显著特征,并增强其他判别性区域细微特征的提取;然后,通过通道权重信息与像素互补信息获得通道与像素多尺度融合特征,以增强不同判别性区域特征的多样性与丰富性。实验结果表明,所提算法在数据集CUB-200-2011上达到89.52%的Top-1准确率、98.46%的Top-5准确率;在Stanford Cars数据集上达到94.64%的Top-1准确率、98.62%的Top-5准确率;在飞行器细粒度分类（FGVCAircraft）数据集上达到93.20%的Top-1准确率、97.98%的Top-5准确率。与循环协同注意力特征学习网络PCA-Net(...     

可见光-近红外HSV图像融合的场景类字典稀疏识别方法

针对典型自然场景智能观测的需求,为提高稀疏分类器在小样本数据库上的识别精度,提出一种可见光和近红外（NIR）HSV图像融合的场景类字典稀疏识别方法。首先,利用一直应用在计算机视觉显示领域中的图像HSV伪彩色处理技术将近红外图像与可见光图像融合;然后,对融合图像进行通用搜索树（GiST）特征和分层梯度方向直方图（PHOG）特征的提取与融合;最后,结合提出的类字典稀疏识别方法得到场景分类结果。所提方法在RGB-NIR数据库上的实验识别精度达到了74.75%。实验结果表明,融合近红外信息的场景图像的识别精度高于未融合时的识别精度,所提方法能够有效增加稀疏识别框架下场景目标的信息表征质量。     

基于MCA与判别字典学习的场景图文字检测方法

传统的文字检测方法在场景图像复杂背景、噪声污染和文字的多种形态特征的干扰下,检测的准确率很低,漏检、误检非常严重.针对这些问题,提出了基于形态成分分析(MCA)与判别字典学习的场景图像文字检测的方法.通过学习过完备字典将文字检测问题转化成稀疏和鲁棒表示的问题.利用MCA与改进的Fisher判别准则学习一个过完备字典,求解待检测图像文字部分的稀疏系数,重建待检测图像中的文字图像,进行文字检测.通过在ICDAR2003/2005/2011和MSRA-TDS00数据库中的大量的实验证明了与其他文字检测方法相比,该方法能有效提高检测准确率.     

基于区域特征融合网络的群组行为识别

针对目前群组行为识别方法未充分利用场景空间信息和计算复杂度较高的问题,文中提出基于区域特征融合的群组行为识别方法.首先,利用卷积神经网络提取场景的区域特征.再将场景区域特征依据空间位置分离、排列、组合成一系列区域特征序列.最后,利用长短时记忆网络融合区域特征序列,同时综合多层次和多模态策略提升方法效果.在Collective、Volleyball数据集上的实验表明文中方法性能较优.     

利用改进型VGG标签学习的表情识别方法

针对图像表情判别精度低下的问题,提出一种基于改进型VGG-16网络的人脸表情识别方法。为解决传统方法存在像素特征分布不均的问题,采用基于改进的高斯混合模型进行图像特征数据的有效提取;基于改进的VGG-16深度神经网络,增强人脸表情识别的训练样本,实现对采集的图像数据多表情多场景精准区分。基于通用数据集及自采集数据集进行仿真实验,验证所提方法在表情识别的准确度和速度方面都展现出一定优势,尤其在黑暗条件下识别准确率可达90%左右。     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

Call for Papers The 6^th International Conference on Grid and Cooperative Computing

Recent years have seen rapid advances in various grid-related technologies, middleware, and applications. The GCC conference has become one of the largest scientific events worldwide in grid and cooperative computing. The 6th international conference on grid and cooperative computing (GCC2007) Sponsored by China Computer Federation (CCF),Institute of Computing Technology, Chinese Academy of Sciences (ICT) and Xinjiang University ,and in Cooperation with IEEE Computer Soceity ,is to be held from August 16 to 18, 2007 in Urumchi, Xinjiang, China.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

The Distributed Time-Triggered Simulation Scheme: Core Principles and Supporting Execution Engine

Distributed real-time simulation is a young technology field but its practice is under increasing demands. In recent years the author and his collaborators have been establishing a new approach called the distributed time-triggered simulation (DTS) scheme which is conceptually simple and easy to use but widely applicable. The concept was initiated in the course of developing a new-generation object-oriented real-time programming scheme called the time-triggered message-triggered object (TMO) programming scheme. Some fundamental issues inherent in distributed real-time simulation that were learned during recent experimental studies are discussed along with some approaches for resolving the issues. An execution engine developed to support both the TMOs engaged in control computation and the TMOs engaged in DTS is also discussed along with its possible extensions that will enable significantly larger-scale DTSs.     

Software-Konfigurationsmanagement in großen Softwareprojekten

Zusammenfassung Mit zunehmender Größe der Softwaresysteme verschärfen sich die für die Software-Herstellung typischen Probleme: Beherrschen großer Objektmengen, Erhalten der Systemkonsistenz, Kontrolle der ständigen Änderungseinflüsse und Gewährleisten einer langen Lebensdauer. Die Disziplin Konfigurationsmanagement bildet den methodischen Ansatz, diese Probleme besser zu beherrschen. Software-Konfigurationsmanagement faßt die Herstellung von Softwaresystemen als eine Abfolge von kontrollierten Änderungen an gesicherten Zwischen- und Endergebnissen auf. Dargestellt werden die Objekte und Funktionen des Software-Konfigurationsmanagements sowie die hierfür in großen Software-Projekten benötigten Methoden, Instanzen und Hilfsmittel.     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and     

Editorial:Brain-machine interface(BMI) and cyborg intelligence

正Brain-machine interfaces(BMIs)aim at building a direct communication pathway between the brain and an external device,and represent an area of research where significant progress has been made during the past decade.Based on BMIs,mind information can be read out by neural signals to control     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate     

SCIENCE CHINA Information Sciences SUBJECT INDEX TO VOLUME 57(2014)

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