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

4,423 篇 会议
232 篇 期刊文献
55 册 图书
10 篇 学位论文
3 篇 资讯

馆藏范围

4,723 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,622 篇 工学
- 1,500 篇 计算机科学与技术...
- 885 篇 电气工程
- 773 篇 软件工程
- 463 篇 信息与通信工程
- 331 篇 控制科学与工程
- 292 篇 生物医学工程（可授...
- 223 篇 光学工程
- 210 篇 机械工程
- 162 篇 电子科学与技术（可...
- 143 篇 仪器科学与技术
- 126 篇 生物工程
- 89 篇 材料科学与工程（可...
- 68 篇 测绘科学与技术
- 58 篇 化学工程与技术
- 32 篇 土木工程
- 27 篇 建筑学
- 25 篇 力学（可授工学、理...
1,484 篇 理学
- 925 篇 物理学
- 656 篇 数学
- 431 篇 统计学（可授理学、...
- 228 篇 系统科学
- 139 篇 生物学
- 64 篇 化学
759 篇 医学
- 594 篇 临床医学
- 222 篇 特种医学
- 78 篇 基础医学(可授医学...
- 44 篇 药学(可授医学、理...
214 篇 管理学
- 119 篇 管理科学与工程(可...
- 103 篇 图书情报与档案管...
- 25 篇 工商管理
24 篇 农学
17 篇 法学
13 篇 教育学
12 篇 经济学
10 篇 艺术学
9 篇 文学
2 篇 军事学

主题

548 篇 image processing
461 篇 image segmentati...
390 篇 stochastic proce...
365 篇 statistical anal...
259 篇 feature extracti...
231 篇 bayesian methods
224 篇 image reconstruc...
188 篇 signal processin...
187 篇 image analysis
141 篇 pixel
135 篇 image restoratio...
109 篇 statistics
106 篇 pattern recognit...
105 篇 image edge detec...
103 篇 hidden markov mo...
103 篇 image coding
99 篇 noise reduction
96 篇 testing
96 篇 robustness
95 篇 histograms

机构

10 篇 school of electr...
8 篇 indian statistic...
7 篇 univ penn dept r...
7 篇 department of el...
7 篇 department of el...
7 篇 univ waterloo de...
7 篇 univ michigan de...
6 篇 nanyang technol ...
6 篇 smart computer v...
6 篇 computer vision ...
5 篇 georgia inst tec...
5 篇 microsoft resear...
5 篇 carnegie mellon ...
5 篇 signal and image...
5 篇 peng cheng lab p...
4 篇 google res mount...
4 篇 texas a&m univ.
4 篇 department of sy...
4 篇 ivan kozhedub kh...
4 篇 tokyo metropolit...

作者

9 篇 landman bennett ...
8 篇 vorapoj patanavi...
8 篇 dougherty er
8 篇 h. krim
7 篇 yannick berthoum...
7 篇 a.s. willsky
7 篇 sang hwa lee
6 篇 astola jaakko t.
6 篇 prince jerry l.
6 篇 umapada pal
6 篇 alexander wong
6 篇 udupa jk
6 篇 nam ik cho
6 篇 w. pieczynski
6 篇 fessler jeffrey ...
6 篇 w.c. karl
6 篇 pesquet jean-chr...
6 篇 pal umapada
6 篇 jong-il park
6 篇 pieczynski w

语言

4,631 篇 英文
49 篇 其他
31 篇 中文
13 篇 土耳其文
1 篇 西班牙文
1 篇 法文
1 篇 俄文

检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4723 条记录，以下是791-800 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：

SUBJECTIVE ASSESSMENT OF image QUALITY INDUCED SALIENCY VARIATION 26

SUBJECTIVE ASSESSMENT OF IMAGE QUALITY INDUCED SALIENCY VARI...

引用

26th IEEE International conference on image processing (ICIP)

作者： Leveque, Lucie Zhang, Wei Liu, Hantao Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou Peoples R China Xidian Univ Sch Telecommun Engn Xian Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781538662496

Our previous study has shown that image distortions cause saliency distraction, and that visual saliency of a distorted image differs from that of its distortion-free reference. Being able to measure such distortion-induced saliency variation (DSV) significantly benefits algorithms for automated image quality assessment. methods of quantifying DSV, however, remain unexplored due to the lack of a benchmark. In this paper, we build a benchmark for the measurement of DSV through a subjective study. Sixteen experts in computer vision were asked to compare saliency maps of distorted images to the corresponding saliency maps of the original images. All saliency maps were rendered from ground truth human fixations. A statistical analysis is performed to reveal the behaviours and properties of human assessment of the saliency variation. The benchmark is made publicly available to the research community.

关键词： image quality distortion eye-tracking saliency visual attention

来源：评论

学校读者我要写书评

暂无评论

Robust Binary Loss for Multi-Category Classification with Label Noise

Robust Binary Loss for Multi-Category Classification with La...

引用

IEEE International conference on Acoustics, Speech and Signal processing

作者： Defu Liu Guowu Yang Jinzhao Wu Jiayi Zhao Fengmao Lv School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Mathematics and Physics Guangxi University for Nationalities Nanning China Center of Statistical Research & School of Statistics Southwestern University of Finance and Economics Chengdu China

Deep learning has achieved tremendous success in image classification. However, the corresponding performance leap relies heavily on large-scale accurate annotations, which are usually hard to collect in reality. It is essential to explore methods that can train deep models effectively under label noise. To address the problem, we propose to train deep models with robust binary loss functions. To be specific, we tackle the K-class classification task by using K binary classifiers. We can immediately use multi-category large margin classification approaches, e.g., Pairwise-Comparison (PC) or One-Versus-All (OVA), to jointly train the binary classifiers for multi-category classification. Our method can be robust to label noise if symmetric functions, e.g., the sigmoid loss or the ramp loss, are employed as the binary loss function in the framework of risk minimization. The learning theory reveals that our method can be inherently tolerant to label noise in multi-category classification tasks. Extensive experiments over different datasets with different types of label noise are conducted. The experimental results clearly confirm the effectiveness of our method.

关键词： Training Deep learning conferences Speech recognition Signal processing Robustness Risk management

来源：评论

学校读者我要写书评

暂无评论

Arbitrary Style Transfer Using Graph Instance Normalization

Arbitrary Style Transfer Using Graph Instance Normalization

引用

IEEE International conference on image processing

作者： Dongki Jung Seunghan Yang Jaehoon Choi Changick Kim Korea Advanced Institute of Science and Technology (KAIST) Daejeon Korea

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization (AdaIN) whitens the source images and applies the style of target images through normalizing the mean and variance of features. However, computing feature statistics for each instance would neglect the inherent relationship between features, so it is hard to learn global styles while fitting to the individual training dataset. In this paper, we present a novel learnable normalization technique for style transfer using graph convolutional networks, termed Graph Instance Normalization (GrIN). This algorithm makes the style transfer approach more robust by taking into account similar information shared between instances. Besides, this simple module is also applicable to other tasks like image-to-image translation or domain adaptation.

关键词： Standards Task analysis Training Smoothing methods Correlation Convolution statistical analysis

来源：评论

学校读者我要写书评

暂无评论

Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations 21

Assessing Robustness of ML-Based Program Analysis Tools usin...

引用

IEEE International conference on Automated Software Engineering (ASE)

作者： Leonhard Applis Annibale Panichella Arie van Deursen Technische Universiteit Delft Netherlands

ISBN: (纸本)9781665447843

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition tasks. Hence, these approaches cannot be applied to ML targeting program analysis tasks. In this paper, we extend metamorphic testing approaches for ML models targeting software programs. We present LAMPION, a novel testing framework that applies (semantics preserving) metamorphic transformations on the test datasets. LAMPION produces new code snippets equivalent to the original test set but different in their identifiers or syntactic structure. We evaluate LAMPION against CodeBERT, a state-of-the-art ML model for Code-To-Text tasks that creates Javadoc summaries for given Java methods. Our results show that simple transformations significantly impact the target model behavior, providing additional information on the models reasoning apart from the classic performance metric.

关键词： Measurement Java statistical analysis Semantics Syntactics Robustness Software

来源：评论

学校读者我要写书评

暂无评论

LOW DOSE ABDOMINAL CT image RECONSTRUCTION: AN UNSUPERVISED LEARNING BASED APPROACH 26

LOW DOSE ABDOMINAL CT IMAGE RECONSTRUCTION: AN UNSUPERVISED ...

引用

26th IEEE International conference on image processing (ICIP)

作者： Kuanar, Shiba Athitsos, Vassilis Mahapatra, Dwarikanath Rao, K. R. Akhtar, Zahid Dasgupta, Dipankar Univ Texas Arlington Dept Elect Arlington TX 76019 USA Univ Texas Arlington Dept Comp Sci Engn Arlington TX 76019 USA IBM Res Melbourne Vic Australia Univ Memphis Dept Comp Sci Memphis TN 38152 USA

ISBN: (纸本)9781538662496

In medical practice, the X-ray Computed tomography-based scans expose a high radiation dose and lead to the risk of prostate or abdomen cancers. On the other hand, the low-dose CT scan can reduce radiation exposure to the patient. But the reduced radiation dose degrades image quality for human perception, and adversely affects the radiologist's diagnosis and prognosis. In this paper, we introduce a GAN based auto-encoder network to de-noise the CT images. Our network first maps CT images to low dimensional manifolds and then restore the images from its corresponding manifold representations. Our reconstruction algorithm separately calculates perceptual similarity, learns the latent feature maps, and achieves more accurate and visually pleasing reconstructions. We also showed the effectiveness of our model on a number of patient abdomen CT images, and compare our results with existing deep learning and iterative reconstruction methods. Experimental results demonstrate that our model outperforms other state-of-the-art methods in terms of PSNR, SSIM, and statistical properties of the image regions.

关键词： Auto-encoder Low dose CT image De-noise Manifold GAN

来源：评论

学校读者我要写书评

暂无评论

A Novel Centroid Update Approach For Clustering-Based Superpixel methods And Superpixel-Based Edge Detection

A Novel Centroid Update Approach For Clustering-Based Superp...

引用

IEEE International conference on image processing

作者： Houwang Zhang Chong Wu Le Zhang Hanying Zheng School of Automation China University of Geosciences Wuhan China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Superpixel is widely used in image processing. And among the methods for superpixel generation, clustering-based methods have a high speed and a good performance at the same time. However, most clustering-based superpixel methods are sensitive to noise. To solve these problems, in this paper, we first analyze the features of noise. Then according to the statistical features of noise, we propose a novel centroid update approach to enhance the robustness of clustering-based superpixel methods. Besides, we propose a novel superpixel-based edge detection method. The experiments on BSD500 dataset show that our approach can significantly enhance the performance of clustering-based superpixel methods in noisy environment. Moreover, we also show that our proposed edge detection method outperforms other classical methods.

关键词： image edge detection image color analysis Noise measurement Colored noise Silicon Gaussian noise image segmentation

来源：评论

学校读者我要写书评

暂无评论

DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis

DeepMMSA: A Novel Multimodal Deep Learning Method for Non-sm...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Yujiao Wu Jie Ma Xiaoshui Huang Sai Ho Ling Steven Weidong Su Faculty of Engineering and IT Biomedical Engineering University of Technology Sydney Ultimo NSW Australia Australian Artificial Intelligence Institute (AAII) Faculty of Engineering and IT University of Technology Sydney Broadway NSW Australia ACRF Image X Institute Faculty of Medicine and Health University of Sydney Eveleigh NSW Australia

ISBN: (纸本)9781665442084

Lung cancer is the leading cause of cancer death worldwide. The critical reason for the deaths is delayed diagnosis and poor prognosis. With the accelerated development of deep learning techniques, it has been successfully applied extensively in many real-world applications, including health sectors such as medical image interpretation and disease diagnosis. By combining more modalities that being engaged in the processing of information, multimodal learning can extract better features and improve the predictive ability. The conventional methods for lung cancer survival analysis normally utilize clinical data and only provide a statistical probability. To improve the survival prediction accuracy and help prognostic decision-making in clinical practice for medical experts, we for the first time propose a multimodal deep learning framework for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA. This framework leverages CT images in combination with clinical data, enabling the abundant information held within medical images to be associate with lung cancer survival information. We validate our model on the data of 422 NSCLC patients from The Cancer Imaging Archive (TCIA). Experimental results support our hypothesis that there is an underlying relationship between prognostic information and radiomic images. Besides, quantitative results show that our method could surpass the state-of-the-art methods by 4% on concordance.

关键词： Deep learning Decision making Lung cancer Probability Feature extraction Data models Medical diagnosis

来源：评论

学校读者我要写书评

暂无评论

Deep Product Quantization Module for Efficient image Retrieval

Deep Product Quantization Module for Efficient Image Retriev...

引用

IEEE International conference on Acoustics, Speech and Signal processing

作者： Meihan Liu Yongxing Dai Yan Bai Ling-Yu Duan Institute of Digital Media Peking University China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft quantization with specially designed deep neural architectures. We propose a simple but effective deep Product Quantization Module (PQM) to jointly learn discriminative codebook and precise hard assignment in an end-to-end manner. In this work, we use the straight-through estimator to make it feasible to directly optimize the discrete binary representations in deep neural networks with stochastic gradient descent. Different from previous deep vector quantization methods, PQM is a plug-and-play module which can be adaptive to various base networks in the scenarios of image search or compression. Besides, we propose a reconstruction loss to minimize the domain gap between the original embedding features and codebook. Experimental results show that PQM outperforms state-of-the-art deep supervised hashing and quantization methods on several image retrieval benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Recent Advances in Overcoming the Independence Assumption in Adaptive Filtering Analyses

Recent Advances in Overcoming the Independence Assumption in...

引用

International conference on Systems, Signals and image processing, IWSSIP

作者： Thiago T. P. Silva Pedro Lara Thiago Gonzaga Diego Brandão Luís Tarrataca Diego B. Haddad CEFET/RJ Brazil

ISBN: (数字)9781728175393

ISBN: (纸本)9781728175409

One important strength of adaptive filtering algorithms is their ability to learn from their interactions with the environment. Such a flexibility comes with a price, since their performance strongly depends on adjustable parameters. In order to provide to the designer guidelines and guaranties for both performance and stability, the open literature often relies on stochastic models that predict the learning capabilities of different algorithms. Unfortunately, usually some statistical hypotheses are assumed to make the mathematics tractable. The most critical of them is the independence assumption, which states that the adaptive coefficients are statistically independent from the excitation data. Such an assumption hampers the accuracy of the theoretical predictions in the case of large step sizes, which is a common choice in practice. Fortunately, a technique that avoids the independence assumption, respectively exact expectation analysis, has experienced some advances. In this paper, it is demonstrated that such recent advances can be used to address a complex configuration of the least mean squares algorithm, where the additive noise is colored and the ideal plant the adaptive scheme intends to estimate is nonlinear.

关键词： stochastic processes Mathematical model Standards Adaptation models Additive noise Convergence Asymptotic stability

来源：评论

学校读者我要写书评

暂无评论

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

引用

IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Feng Xiao Cong Shen Yu Chen Tian Yang Shengyong Chen Zhijun Liao Jijun Tang School of Computer Science and Engineering Tianjin University of Technology Tianjin P.R.China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin P.R.China School of Basic Medical Sciences Fujian Medical University Fuzhou Fujian P.R.China College of Intelligence and Computing Tianjin University Tianjin P.R.China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin P.R.China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong P.R.China University of South Carolina Columbia SC U.S.A.

ISBN: (纸本)9781665429825

Drawing support from an effective Medical image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.

关键词： Deep learning image segmentation statistical analysis Lung Predictive models Feature extraction Generative adversarial networks

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473页 << < 76 77 78 79 80 81 82 83 84 85 > >>

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：