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

676 篇 会议
230 篇 期刊文献
7 册 图书

馆藏范围

913 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

421 篇 工学
- 258 篇 计算机科学与技术...
- 223 篇 软件工程
- 132 篇 光学工程
- 102 篇 电气工程
- 100 篇 生物医学工程（可授...
- 83 篇 信息与通信工程
- 66 篇 电子科学与技术（可...
- 66 篇 生物工程
- 37 篇 控制科学与工程
- 32 篇 机械工程
- 30 篇 仪器科学与技术
- 18 篇 化学工程与技术
- 13 篇 土木工程
- 12 篇 材料科学与工程（可...
- 11 篇 建筑学
- 9 篇 安全科学与工程
- 8 篇 航空宇航科学与技...
277 篇 理学
- 133 篇 物理学
- 125 篇 数学
- 79 篇 生物学
- 55 篇 统计学（可授理学、...
- 19 篇 化学
- 10 篇 系统科学
74 篇 管理学
- 41 篇 管理科学与工程(可...
- 37 篇 图书情报与档案管...
- 15 篇 工商管理
67 篇 医学
- 61 篇 临床医学
- 48 篇 基础医学(可授医学...
- 32 篇 药学(可授医学、理...
- 9 篇 公共卫生与预防医...
9 篇 艺术学
7 篇 农学
5 篇 教育学
4 篇 法学
1 篇 经济学

主题

137 篇 computer vision
117 篇 laboratories
106 篇 image segmentati...
84 篇 image analysis
63 篇 image processing
54 篇 feature extracti...
53 篇 magnetic resonan...
53 篇 shape
48 篇 cameras
47 篇 pixel
45 篇 image reconstruc...
38 篇 humans
38 篇 layout
35 篇 image edge detec...
34 篇 machine vision
34 篇 biomedical imagi...
33 篇 data mining
33 篇 algorithm design...
32 篇 robustness
32 篇 principal compon...

机构

19 篇 control and inte...
12 篇 department of el...
10 篇 computer vision ...
10 篇 remote sensing s...
10 篇 computer vision ...
10 篇 image analysis a...
10 篇 remote sensing s...
9 篇 radiology image ...
9 篇 department of ra...
9 篇 department of ra...
9 篇 computational in...
8 篇 remote sensing s...
8 篇 centre for medic...
8 篇 department of qu...
8 篇 image analysis l...
8 篇 computer vision ...
8 篇 department of el...
8 篇 laboratory for v...
7 篇 computer vision ...
7 篇 computer vision ...

作者

29 篇 hamid soltanian-...
20 篇 chein-i chang
18 篇 a.c. bovik
17 篇 soltanian-zadeh ...
15 篇 s. mitra
14 篇 h. knutsson
14 篇 aly a. farag
13 篇 timofte radu
13 篇 a.a. farag
13 篇 chang chein-i
12 篇 m.m. trivedi
12 篇 h. soltanian-zad...
11 篇 chang chein-i.
9 篇 bakas spyridon
9 篇 j. goutsias
9 篇 felsberg michael
9 篇 e.j. delp
8 篇 kofler florian
8 篇 d. poussart
8 篇 menze bjoern

语言

875 篇 英文
37 篇 其他
1 篇 中文

检索条件"机构=Computer Vision & Image Analysis Laboratory Department of Electrical and Computer Engineering"

共 913 条记录，以下是361-370 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：

Automated generation of an efficient MPEG-4 Reconfigurable Video Coding decoder implementation

Automated generation of an efficient MPEG-4 Reconfigurable V...

引用

2010 Conference on Design and Architectures for Signal and image Processing, DASIP2010

作者： Gu, Ruirui Piat, Jonathan Raulet, Mickael Janneck, Jorn W. Bhattacharyya, Shuvra S. Department of Electrical and Computer Engineering University of Maryland College Park MD 20742 United States IETR Laboratory UMR CNRS 6164 Image and Remote Sensing Group 35043 Rennes Cedex France United Technologies Research Center Berkeley CA United States

ISBN: (纸本)9781424487356

This paper proposes an automatic design flow from user-friendly design to efficient implementation of video processing systems. This design flow starts with the use of coarse-grain dataflow representations based on the CAL language, which is a complete language for dataflow programming of embedded systems. Our approach integrates previously developed techniques for detecting synchronous dataflow (SDF) regions within larger CAL networks, and exploiting the static structure of such regions using analysis tools in The Dataflow interchange format Package (TDP). Using a new XML format that we have developed to exchange dataflow information between different dataflow tools, we explore systematic implementation of signal processing systems using CAL, SDF-like region detection, TDP-based static scheduling, and CAL-to-C (CAL2C) translation. Our approach, which is a novel integration of three complementary dataflow tools - the CAL parser, TDP, and CAL2C - is demonstrated on an MPEG Reconfigurable Video Coding (RVC) decoder. © 2010 IEEE.

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

引用

International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A novel Markov random field based deformable model for face recognition

A novel Markov random field based deformable model for face ...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Shu Liao Albert C.S. Chung Department of Computer Science and Engineering Lo Kwee-Seong Medical Image Analysis Laboratory Hong Kong University of Science and Technology Hong Kong China

In this paper, a new scheme to address the face recognition problem is proposed. Different from traditional face recognition approaches which represent each facial image by a single feature vector as the classification problem, the proposed method establishes a new way to formulate the face recognition problem as a deformable image registration problem. The main contributions of the paper lie in the following aspects: (i) Each pixel is represented by an anatomical feature signature calculated from its corresponding best scale salient region by using a new salient region detector based on the survival exponential entropy (SEE); (ii) The face recognition problem is formulated as a deformable image registration problem, the deformation model is represented by a Markov random field (MRF) labeling framework. Explicit pixel correspondence is established by the deformation framework. (iii) The survival exponential entropy based normalized mutual information (SEE-NMI) is proposed and integrated with the MRF based deformation model as the similarity measure to reflect the similarity between two facial images. The proposed method is evaluated on the FERET and FRGC version 2 databases and compared with several state-of-the-art face recognition approaches. Experimental results show that the proposed method achieves the highest recognition rate among all the compared approaches.

关键词： Markov random fields Deformable models Face recognition image registration Pixel Entropy Face detection Detectors Labeling Mutual information

来源：评论

学校读者我要写书评

暂无评论

Comparison of voxel-based morphometry (VBM) and tractography of diffusion tensor MRI (DT-MRI) in temporal lobe epilepsy

Comparison of voxel-based morphometry (VBM) and tractography...

引用

Iranian Conference on electrical engineering (ICEE)

作者： Maryam Afzali Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

ISBN: (纸本)9781424467600

Temporal lobe epilepsy (TLE) is a neurological disease that affects millions of individuals in the world. Majority of TLE patients suffer from refractory seizures. Determining abnormal/damaged regions of the brain that harbor seizure focus is a critical step towards treatment of TLE patients. Diffusion tensor imaging (DTI) is a non-invasive method that we use in this work to determine damaged regions of white matter in the brain. To this end, we apply two methods, voxel based morphometry (VBM) and fiber tracking. We use these methods along with two anisotropy maps (EAR and FA) extracted from the DTI data. We use DTI data of 31 subjects (12 normal subjects and 19 TLE patients) for testing and evaluation studies. The VBM method shows that TLE is associated with reduction of both of FA and EAR values in several regions within the temporal lobes. The tractography method shows abnormal diffusion characteristics in three fiber bundles (fornix, uncinate fasciculus, and cingulum). The results show that the EAR maps are less sensitive to noise than the FA maps and the EAR values change more than the FA values in TLE.

关键词： Tensile stress Magnetic resonance imaging Diffusion tensor imaging Temporal lobe Epilepsy Ear Optical fiber testing Diseases Focusing Medical treatment

来源：评论

学校读者我要写书评

暂无评论

Independent Component Discriminant analysis for hyperspectral image classification

Independent Component Discriminant Analysis for hyperspectra...

引用

Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： A. Villa J. A. Benediktsson J. Chanussot C. Jutten GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Faculty of Electrical and Computer Engineering University of Iceland Iceland

In this paper, the use of Independent Component Discriminant analysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Independent Component analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment. The obtained results are compared with one of the most used classifier of hyperspectral images (Support Vector Machine) and show the comparative effectiveness of the proposed method.

关键词： Support vector machines Training Hyperspectral imaging Kernel Accuracy Estimation

来源：评论

学校读者我要写书评

暂无评论

Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

引用

International Conference on Intelligent and Advanced Systems, ICIAS

作者： Mahdi Ramezani Amin Heidari Emad Fatemizadeh Hamid Soltanian-Zadeh Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearest-neighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation analysis (CCA) and the spatial Independent Component analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Clustering algorithms Algorithm design and analysis Correlation Approximation methods Independent component analysis Time series analysis Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Spectral clustering of resting state fMRI reveals default mode network with specifically reduced network homogeneity in major depression

Spectral clustering of resting state fMRI reveals default mo...

引用

Iranian Conference of Biomedical engineering (ICBME)

作者： Alireza Sojoudi Seyed Mohammad Shams Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Bernhard Bogerts Kolja Schiltz Martin Walter Control & Intelligent Centre of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA Department of Psychiatry University of Magdeburg Magdeburg Germany

Although resting state fMRI seems an ideal tool for investigating clinical populations, especially in case of reduced cooperation or tolerance, unbiased methods with high sensitivity for disease relevant pathologies remain to be identified. In this paper, we perform spectral clustering on the mean time series of automated anatomical labeling regions of interest for comparing the resting state networks in healthy volunteers and major depression disorder (MDD) patients. A new network homogeneity measure is suggested as a criterion for evaluating the level of homogeneity in a network. We found reduced network homogeneity specifically within the default mode network in MDD subjects compared to age-matched controls. In contrast to previously proposed methods investigating network homogeneity, we fully relied on data-driven definition of clusters of interest to fill an important gap between ROI based network analyses and those using ICA.

关键词： Smoothing methods

来源：评论

学校读者我要写书评

暂无评论

Applying deep-layered clustering to mammography image analytics

Applying deep-layered clustering to mammography image analyt...

引用

Annual ORNL Biomedical Science & engineering Conference (BSEC)

作者： Derek C. Rose Itamar Arel Thomas P. Karnowski Vincent C. Paquit Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Oak Ridge National Laboratory Machine Vision Group Oak Ridge USA

ISBN: (纸本)9781424467136;9781424467143

This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem; minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and health institutions. This research examines a segmented look at mammograms for computer aided detection with the goal of reliably labeling regions of interest requiring the attention of a radiologist. Classification is achieved by employing the building blocks, namely unsupervised clustering, of a deep learning architecture in tandem with a standard feed-forward neural network. Early results show promise for creating a classification engine that handles high-dimensional data with minimum engineering of image features, with a per-image patch sensitivity of 0.96 and specificity of 0.99.

关键词： Mammography image analysis Breast cancer Cancer detection Costs image segmentation computer network reliability Labeling computer architecture Feedforward systems

来源：评论

学校读者我要写书评

暂无评论

Attribute ranking for lateralizing focal epileptogenicity in temporal lobe epilepsy

Attribute ranking for lateralizing focal epileptogenicity in...

引用

Iranian Conference of Biomedical engineering (ICBME)

作者： Shobeir Fakhraei Hamid Soltanian-Zadeh Kost Elisevich Farshad Fotouhi Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA CIPCE School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

A consensus feature-ranking approach has been applied to the study of localization-related temporal lobe epilepsy (TLE) in order to evaluate the relative discriminative power of individual attributes. Cases were selected on the basis of a postoperative outcome free of disabling seizures (i.e., Engel class I) in order to establish a definitive laterality of focal epileptogenicity. Several quantitative measures made available by imaging and electrographic studies are considered and the most discriminative of these are quantitatively prioritized for the lateralization of focal epileptogenicity. Cases requiring extraoperative electrocorticography were examined as a subgroup to establish whether the current method of analysis could distinguish laterality sufficiently well to avoid the requirement for intracranial electrode implantation.

关键词： Electroencephalography Medical diagnostic imaging Epilepsy Data mining Temporal lobe

来源：评论

学校读者我要写书评

暂无评论

Consensus Feature Ranking in Datasets with Missing Values

Consensus Feature Ranking in Datasets with Missing Values

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Shobeir Fakhraei Hamid Soltanian-Zadeh Farshad Fotouhi Kost Elisevich Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Department of Computer Science Wayne State University Detroit MI USA CIPCE School of Electrical And Computer Engineering University of Tehran Tehran Iran Department of Neurosurgery Henry Ford Health System Detroit MI USA

Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must be taken into consideration in the ranking and evaluation phases in order to legitimately apply a feature ranking method. In a comparison study, a Performance Index (PI) is proposed that takes into account both the number of features and the number of samples involved in the classification.

关键词： Accuracy Support vector machines Classification algorithms Niobium Medical diagnostic imaging Data mining

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92页 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：