检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,929 篇 会议
241 册 图书
24 篇 期刊文献

馆藏范围

2,193 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,607 篇 工学
- 1,334 篇 计算机科学与技术...
- 467 篇 软件工程
- 250 篇 电气工程
- 235 篇 机械工程
- 186 篇 光学工程
- 179 篇 信息与通信工程
- 119 篇 控制科学与工程
- 96 篇 生物工程
- 62 篇 生物医学工程（可授...
- 42 篇 仪器科学与技术
- 39 篇 电子科学与技术（可...
- 30 篇 化学工程与技术
- 21 篇 安全科学与工程
- 18 篇 材料科学与工程（可...
- 15 篇 交通运输工程
- 13 篇 建筑学
444 篇 理学
- 257 篇 物理学
- 202 篇 数学
- 110 篇 生物学
- 57 篇 统计学（可授理学、...
- 22 篇 化学
228 篇 医学
- 200 篇 临床医学
- 26 篇 基础医学(可授医学...
- 22 篇 特种医学
137 篇 管理学
- 83 篇 图书情报与档案管...
- 60 篇 管理科学与工程(可...
- 19 篇 工商管理
27 篇 艺术学
- 27 篇 设计学（可授艺术学...
16 篇 农学
- 15 篇 作物学
15 篇 法学
- 13 篇 社会学
9 篇 教育学
7 篇 经济学
5 篇 文学
5 篇 军事学

主题

321 篇 computer vision
286 篇 pattern recognit...
166 篇 artificial intel...
122 篇 computer imaging...
119 篇 feature extracti...
101 篇 image processing...
82 篇 face recognition
68 篇 training
61 篇 object detection
60 篇 image segmentati...
57 篇 computer applica...
55 篇 deep learning
51 篇 robustness
47 篇 computer graphic...
46 篇 cameras
45 篇 visualization
43 篇 semantics
38 篇 machine learning
38 篇 object recogniti...
37 篇 shape

机构

89 篇 univ chinese aca...
67 篇 chinese acad sci...
60 篇 national laborat...
56 篇 chinese acad sci...
50 篇 univ chinese aca...
36 篇 chinese univ hon...
36 篇 university of ch...
31 篇 institute of aut...
27 篇 chinese acad sci...
26 篇 school of artifi...
23 篇 univ sci & techn...
22 篇 chinese academy ...
18 篇 chinese acad sci...
17 篇 chinese univ hon...
16 篇 chinese acad sci...
16 篇 univ chinese aca...
15 篇 national laborat...
15 篇 computer vision ...
14 篇 tsinghua univers...
14 篇 department of in...

作者

32 篇 wang xiaogang
29 篇 lu hanqing
28 篇 tan tieniu
28 篇 wang jinqiao
23 篇 li stan z.
22 篇 pal umapada
21 篇 huang kaiqi
21 篇 lei zhen
21 篇 qiao yu
19 篇 tieniu tan
19 篇 hu weiming
18 篇 tang xiaoou
17 篇 xilin chen
15 篇 wang liang
15 篇 chen xilin
15 篇 cheng jian
14 篇 liu jing
14 篇 tang ming
13 篇 xiaoou tang
13 篇 shiguang shan

语言

2,163 篇 英文
19 篇 中文
17 篇 其他
1 篇 土耳其文

检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2194 条记录，以下是1131-1140 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

34th International conference on computer Safety, Reliability, and Security, SAFECOMP 2015

34th International Conference on Computer Safety, Reliabilit...

引用

34th International conference on computer Safety, Reliability, and Security, SAFECOMP 2015

ISBN: (纸本)9783319242545

the proceedings contain 34 papers. the special focus in this conference is on Flight Systems, Automotive Embedded Systems, Automotive Software, Error Detection and Medical Systems. the topics include: Medical devices, electronic health records and assuring patient safety;modeling guidelines and usage analysis towards applying hip-hops method to airborne electrical systems;the formal derivation of mode logic for autonomous satellite flight formation;simulation of automotive security threat warnings to analyze driver interpretations and emotional transitions;improving dependability of vision-based advanced driver assistance systems using navigation data and checkpoint recognition;model-based analysis for safety critical software;integrated safety analysis using systems-theoretic process analysis and software model checking;back-to-back fault injection testing in model-based development;understanding the effects of data corruption on application behaviour based on data characteristics;a multi-layer anomaly detector for dynamic service-based systems;safety case driven development for medical devices;towards an international security case framework for networked medical devices;systems-theoretic safety assessment of robotic telesurgical systems;risk classification of data transfer in medical systems;requirement engineering for functional alarm system for interoperable medical devices;the safety requirements decomposition pattern;automatic architecture hardening using safety patterns;a model for safety case confidence assessment;quantifying risks to data assets using formal metrics in embedded system design;barriers to the use of intrusion detection systems in safety-critical applications and organisational, political and technical barriers to the integration of safety and cyber-security incident reporting systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Recurrent Neural Network for Skeleton Based Action recognition

Hierarchical Recurrent Neural Network for Skeleton Based Act...

引用

IEEE conference on computer vision and pattern recognition

作者： Yong Du Wei Wang Liang Wang Center for Research on Intelligent Perception and Computing CRIPAC Nat'l Lab of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467369657

Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. the final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.

关键词： Skeletons bone structure recurrent neural nets Human Remains Neural Networks (computer) Subnetwork layer number Computational efficiency representations

来源：评论

学校读者我要写书评

暂无评论

Object-Scene Convolutional Neural Networks for Event recognition in Images

Object-Scene Convolutional Neural Networks for Event Recogni...

引用

IEEE conference on computer vision and pattern recognition Workshops

作者： Limin Wang Zhe Wang Wenbin Du Yu Qiao Department of Information Engineering The Chinese University of Hong Kong Shenzhen key lab of Comp. Vis. & Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China

ISBN: (纸本)9781467367608

Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event recognition in images. this paper addresses the issue of event recognition from images and proposes an effective method with deep neural networks. Specifically, we design a new architecture, called Object-Scene Convolutional Neural Network (OS-CNN). this architecture is decomposed into object net and scene net, which extract useful information for event understanding from the perspective of objects and scene context, respectively. Meanwhile, we investigate different network architectures for OS-CNN design, and adapt the deep (AlexNet) and very-deep (GoogLeNet) networks to the task of event recognition. Fur-thermore, we find that the deep and very-deep networks are complementary to each other. Finally, based on the pro-posed OS-CNN and comparative study of different network architectures, we come up with a solution of five-stream CN-Nfor the track of cultural event recognition at the ChaLearn Looking at People (LAP) challenge 2015. Our method obtains the performance of 85.5% and ranks the 1~(st) place in this challenge.

关键词： Object-Scene Convolutional Neural Networks Event recognition Images

来源：评论

学校读者我要写书评

暂无评论

Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face recognition with Image Sets

Discriminant Analysis on Riemannian Manifold of Gaussian Dis...

引用

IEEE conference on computer vision and pattern recognition

作者： Wen Wang Ruiping Wang Zhiwu Huang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781467369657

this paper presents a method named Discriminant Analysis on Riemannian manifold of Gaussian distributions (DARG) to solve the problem of face recognition with image sets. Our goal is to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as Gaussian Mixture Model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. In the light of information geometry, the Gaussians lie on a specific Riemannian manifold. To encode such Riemannian geometry properly, we investigate several distances between Gaussians and further derive a series of provably positive definite probabilistic kernels. through these kernels, a weighted Kernel Discriminant Analysis is finally devised which treats the Gaussians in GMMs as samples and their prior probabilities as sample weights. the proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.

关键词： Kernel Manifolds Gaussian distribution Face recognition Covariance matrices Data models Adaptation models

来源：评论

学校读者我要写书评

暂无评论

Saliency Detection by Multi-Context Deep Learning

Saliency Detection by Multi-Context Deep Learning

引用

IEEE conference on computer vision and pattern recognition

作者： Rui Zhao Wanli Ouyang Hongsheng Li Xiaogang Wang Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Department of Electronic Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467369657

Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. this issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework. To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.

关键词： Object detection Neural network Image classification initialization Frameworks

来源：评论

学校读者我要写书评

暂无评论

Photometric Stereo with Near Point Lighting: A Solution by Mesh Deformation

Photometric Stereo with Near Point Lighting: A Solution by M...

引用

IEEE conference on computer vision and pattern recognition

作者： Wuyuan Xie Chengkai Dai Charlie C. L. Wang Department of Mechanical and Automation Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467369657

We tackle the problem of photometric stereo under near point lighting in this paper. Different from the conventional formulation of photometric stereo that assumes parallel lighting, photometric stereo under the near point lighting condition is a nonlinear problem as the local surface normals are coupled with its distance to the camera as well as the light sources. To solve this non-linear problem of PS with near point lighting, a local/global mesh deformation approach is developed in our work to determine the position and the orientation of a facet simultaneously, where each facet is corresponding to a pixel in the image captured by the camera. Unlike nonlinear optimization schemes, the mesh deformation in our approach is decoupled into an iteration of interlaced steps of local projection and global blending. Experimental results verify that our method can generate accurate estimation of surface shape under near point lighting in a few iterations. Besides, this approach is robust to errors on the positions of light sources and is easy to be implemented.

关键词： Ocular accommodation near point Lighting Photomechanics Cameras LIGHT SOURCES Nonlinear systems surface normal PIXEL IMAGE iterative methods Medical Subject Headings

来源：评论

学校读者我要写书评

暂无评论

Understanding Pedestrian Behaviors from Stationary Crowd Groups

Understanding Pedestrian Behaviors from Stationary Crowd Gro...

引用

IEEE conference on computer vision and pattern recognition

作者： Shuai Yi Hongsheng Li Xiaogang Wang Dept. of Electron. Eng. Chinese Univ. of Hong Kong Hong Kong China

ISBN: (纸本)9781467369657

Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance. Stationary crowd groups are a key factor influencing pedestrian walking patterns but was largely ignored in literature. In this paper, a novel model is proposed for pedestrian behavior modeling by including stationary crowd groups as a key component. through inference on the interactions between stationary crowd groups and pedestrians, our model can be used to investigate pedestrian behaviors. the effectiveness of the proposed model is demonstrated through multiple applications, including walking path prediction, destination prediction, personality classification, and abnormal event detection. To evaluate our model, a large pedestrian walking route dataset is built. the walking routes of 12, 684 pedestrians from a one-hour crowd surveillance video are manually annotated. It will be released to the public and benefit future research on pedestrian behavior analysis and crowd scene understanding.

关键词： Pedestrian Behavior Pedestrians role model video monitoring walking Crucial components behavioral research Event detection

来源：评论

学校读者我要写书评

暂无评论

Deeply learned face representations are sparse, selective, and robust

Deeply learned face representations are sparse, selective, a...

引用

IEEE conference on computer vision and pattern recognition

作者： Yi Sun Xiaogang Wang Xiaoou Tang Dept. of Inf. Eng. Chinese Univ. of Hong Kong Hong Kong China

ISBN: (纸本)9781467369657

this paper designs a high-performance deep convolutional network (DeepID2+) for face recognition. It is learned with the identification-verification supervisory signal. By increasing the dimension of hidden representations and adding supervision to early convolutional layers, DeepID2+ achieves new state-of-the-art on LFW and YouTube Faces benchmarks. through empirical studies, we have discovered three properties of its deep neural activations critical for the high performance: sparsity, selectiveness and robustness. (1) It is observed that neural activations are moderately sparse. Moderate sparsity maximizes the discriminative power of the deep net as well as the distance between images. It is surprising that DeepID2+ still can achieve high recognition accuracy even after the neural responses are binarized. (2) Its neurons in higher layers are highly selective to identities and identity-related attributes. We can identify different subsets of neurons which are either constantly excited or inhibited when different identities or attributes are present. Although DeepID2+ is not taught to distinguish attributes during training, it has implicitly learned such high-level concepts. (3) It is much more robust to occlusions, although occlusion patterns are not included in the training set.

关键词： Occlusion personal identity Neurons Face Robustness empirical studies representations Facial recognition Face

来源：评论

学校读者我要写书评

暂无评论

Recognize Complex Events from Static Images by Fusing Deep Channels

Recognize Complex Events from Static Images by Fusing Deep C...

引用

IEEE conference on computer vision and pattern recognition

作者： Yuanjun Xiong Kai Zhu Dahua Lin Xiaoou Tang Department of Information Engineering The Chinese University of Hong Kong

ISBN: (纸本)9781467369657

A considerable portion of web images capture events that occur in our personal lives or social activities. In this paper, we aim to develop an effective method for recognizing events from such images. Despite the sheer amount of study on event recognition, most existing methods rely on videos and are not directly applicable to this task. Generally, events are complex phenomena that involve interactions among people and objects, and therefore analysis of event photos requires techniques that can go beyond recognizing individual objects and carry out joint reasoning based on evidences of multiple aspects. Inspired by the recent success of deep learning, we formulate a multi-layer framework to tackle this problem, which takes into account both visual appearance and the interactions among humans and objects, and combines them via semantic fusion. An important issue arising here is that humans and objects discovered by detectors are in the form of bounding boxes, and there is no straightforward way to represent their interactions and incorporate them with a deep network. We address this using a novel strategy that projects the detected instances onto multi-scale spatial maps. On a large dataset with 60, 000 images, the proposed method achieved sub-stantial improvement over the state-of-the-art, raising the accuracy of event recognition by over 10%.

关键词： images bounding boxes Human Videotapes

来源：评论

学校读者我要写书评

暂无评论

Joint Multi-feature Spatial Context for Scene recognition in the Semantic Manifold

Joint Multi-feature Spatial Context for Scene Recognition in...

引用

IEEE conference on computer vision and pattern recognition

作者： Xinhang Song Shuqiang Jiang Luis Herranz Key Lab. of Intell. Inf. Process. of Chinese Acad. of Sci. Inst. of Comput. Technol. Beijing China

ISBN: (纸本)9781467369657

In the semantic multinomial framework patches and images are modeled as points in a semantic probability simplex. Patch theme models are learned resorting to weak supervision via image labels, which leads the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. thus, discovering and modeling these patterns is critical to improve the recognition performance in this representation. In this paper, we observe that not only global co-occurrences at the image-level are important, but also different regions have different category co-occurrence patterns. We exploit local contextual relations to address the problem of discovering consistent co-occurrence patterns and removing noisy ones. Our hypothesis is that a less noisy semantic representation, would greatly help the classifier to model consistent co-occurrences and discriminate better between scene categories. An important advantage of modeling features in a semantic space is that this space is feature independent. thus, we can combine multiple features and spatial neighbors in the same common space, and formulate the problem as minimizing a context-dependent energy. Experimental results show that exploiting different types of contextual relations consistently improves the recognition accuracy. In particular, larger datasets benefit more from the proposed method, leading to very competitive performance.

关键词： spatial context Semantics images recognition (Psychology) Exploration Classifiers

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共220页 << < 110 111 112 113 114 115 116 117 118 119 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：