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

1,071 篇 会议
43 篇 期刊文献
3 册 图书

馆藏范围

1,117 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

614 篇 工学
- 536 篇 计算机科学与技术...
- 406 篇 软件工程
- 143 篇 电气工程
- 51 篇 光学工程
- 32 篇 控制科学与工程
- 30 篇 机械工程
- 29 篇 信息与通信工程
- 29 篇 生物医学工程（可授...
- 28 篇 电子科学与技术（可...
- 14 篇 生物工程
- 10 篇 仪器科学与技术
- 8 篇 交通运输工程
- 7 篇 建筑学
- 6 篇 土木工程
- 6 篇 农业工程
- 5 篇 材料科学与工程（可...
- 5 篇 化学工程与技术
310 篇 医学
- 295 篇 临床医学
- 42 篇 特种医学
- 25 篇 基础医学(可授医学...
- 7 篇 药学(可授医学、理...
162 篇 理学
- 99 篇 数学
- 67 篇 物理学
- 17 篇 生物学
- 12 篇 统计学（可授理学、...
- 5 篇 化学
- 5 篇 系统科学
29 篇 管理学
- 18 篇 图书情报与档案管...
- 12 篇 管理科学与工程(可...
6 篇 农学
- 6 篇 作物学
4 篇 法学
2 篇 教育学
1 篇 经济学

主题

175 篇 image segmentati...
103 篇 image processing
98 篇 feature extracti...
74 篇 computer graphic...
73 篇 shape
72 篇 image color anal...
68 篇 visualization
68 篇 computer vision
64 篇 image reconstruc...
64 篇 training
58 篇 image analysis
51 篇 rendering (compu...
45 篇 robustness
44 篇 graphics
42 篇 image edge detec...
41 篇 cameras
40 篇 layout
38 篇 pixel
37 篇 biomedical imagi...
34 篇 histograms

机构

11 篇 universidade de ...
10 篇 universidade fed...
9 篇 univ estadual ca...
7 篇 institute of com...
7 篇 univ sao paulo i...
6 篇 universidade est...
5 篇 univ fed minas g...
5 篇 impa rio de jane...
4 篇 univ sao paulo i...
4 篇 impa-instituto d...
4 篇 institute of com...
4 篇 géométrica proje...
4 篇 univ fed rio gra...
3 篇 universidade de ...
3 篇 departamento de ...
3 篇 departamento de ...
3 篇 pontificia univ ...
3 篇 univ fed parana ...
3 篇 univ sao paulo i...
3 篇 inst. of comput....

作者

12 篇 velho luiz
11 篇 luiz velho
11 篇 wu shin-ting
11 篇 falcao alexandre...
10 篇 velho l
9 篇 schwartz william...
9 篇 l. velho
9 篇 silvio jamil f. ...
9 篇 miranda paulo a....
8 篇 luis gustavo non...
7 篇 afonso paiva
7 篇 hirata nina s. t...
7 篇 menotti david
7 篇 r.a. lotufo
7 篇 waldemar celes
7 篇 guimaraes pedron...
6 篇 jefersson a. dos...
6 篇 torres ricardo d...
6 篇 laroca rayson
6 篇 william robson s...

语言

1,108 篇 英文
5 篇 其他
3 篇 葡萄牙文
2 篇 中文

检索条件"任意字段=14th Brazilian Symposium on Computer Graphics and Image Processing"

共 1117 条记录，以下是351-360 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：

Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach 37

Enhancing License Plate Super-Resolution: A Layout-Aware and...

引用

37th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Nascimento, Valfride Laroca, Rayson Ribeiro, Rafael O. Schwartz, William Robson Menotti, David Univ Fed Parana Curitiba Parana Brazil Pontificia Univ Catolica Parana Curitiba Parana Brazil Brazilian Fed Police Brasilia DF Brazil Univ Fed Minas Gerais Belo Horizonte MG Brazil

ISBN: (纸本)9798350376043;9798350376036

Despite significant advancements in License Plate Recognition (LPR) through deep learning, most improvements rely on high-resolution images with clear characters. this scenario does not reflect real-world conditions where traffic surveillance often captures low-resolution and blurry images. Under these conditions, characters tend to blend with the background or neighboring characters, making accurate LPR challenging. To address this issue, we introduce a novel loss function, Layout and Character Oriented Focal Loss (LCOFL), which considers factors such as resolution, texture, and structural details, as well as the performance of the LPR task itself. We enhance character feature learning using deformable convolutions and shared weights in an attention module and employ a GAN-based training approach with an Optical Character Recognition (OCR) model as the discriminator to guide the super-resolution process. Our experimental results show significant improvements in character reconstruction quality, outperforming two state-of-the-art methods in both quantitative and qualitative measures. Our code is publicly available at https://***/valfride/lpsr-lacd.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Superpixel segmentation: from theory to applications 36

Superpixel segmentation: from theory to applications

引用

36th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Borlido, Isabela Belem, Felipe Melo, Leonardo theodoro, Taylla M. Silva, Ilan F. do Patrocinio, Zenilton K. G., Jr. Falcao, Alexandre X. Guimardes, Silvio Jamil F. Pontifical Catholic Univ Minas Gerais PUC Minas Comp Sci Dept DCC Image & Multimedia Data Sci Lab IMSci Belo Horizonte MG Brazil Univ Estadual Campinas Inst Comp IC Lab Image Data Sci LIDS UNICAMP Campinas Brazil

ISBN: (纸本)9798350338737;9798350338720

Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. thus, this document presents a proposal for a tutorial to be presented at SIBGRAPI 2023.

关键词： Superpixel Trends Applications

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning-based Approach for Tree Trunk Segmentation 34

A Deep Learning-based Approach for Tree Trunk Segmentation

引用

34th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Jodas, Danilo Samuel Brazolin, Sergio Yojo, Takashi de Lima, Reinaldo Araujo Velasco, Giuliana Del Nero Machado, Aline Ribeiro Papa, Joao Paulo Sao Paulo State Univ Dept Comp BR-17033360 Bauru SP Brazil Univ Sao Paulo Inst Technol Res BR-05508901 Sao Paulo SP Brazil

ISBN: (纸本)9781665423540

Recently, the real-time monitoring of the urban ecosystem has raised the attention of many municipal forestry management services. the proper maintenance of trees is seen as crucial to guarantee the quality and safety of the streetscape. However, the current analysis still involves the time-consuming fieldwork conducted for extracting the measurements of each part of the tree, including the angle and diameter of the trunk, to cite a few. therefore, real-time monitoring is thoroughly necessary for the rapid identification of the constituent parts of the trees in images of the urban environment and the automatic estimation of their physical measures. this paper presents a method to segment the tree trunks in photographs of the municipal regions. To accomplish such a task, we introduce a semantic segmentation convolutional neural network architecture that incorporates a depthwise residual block to the well-known U-Net model to reduce the parameters required to create the network. then, we perform a post-processing step to refine the segmented regions by removing the additional binary areas not related to the tree trunk. Lastly, the proposed method also extracts the central line of the identified region for future computation of the trunk measurements. Compared with the original U-Net architecture, the obtained results confirm the robustness of the proposed approaches, including similar evaluation metrics and the significant reduction of the network size.

关键词： Deep learning convolutional neural networks image processing semantic segmentation urban forest

来源：评论

学校读者我要写书评

暂无评论

Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests 35

Neuroevolution-based Classifiers for Deforestation Detection...

引用

35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Pimenta, Guilherme B. A. Dallaqua, Fernanda B. J. R. Fazenda, Alvaro Faria, Fabio A. Univ Fed Sao Paulo Inst Sci & Technol Sao Jose Dos Campos Brazil Visiona Tecnol Espacial SA Sao Jose Dos Campos Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying a crucial role in the global ecosystem, besides representing the home to countless indigenous peoples. Unfortunately, millions of hectares of tropical forests are lost every year due to deforestation or degradation. To mitigate that fact, monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. these monitoring/detection programs generally use remote sensing images, image processing techniques, machine learning methods, and expert photointerpretation to analyze, identify and quantify possible changes in forest cover. Several projects have proposed different computational approaches, tools, and models to efficiently identify recent deforestation areas, improving deforestation monitoring programs in tropical forests. In this sense, this paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks. Furthermore, a novel framework called e-NEAT has been created and achieved classification results above 90% for balanced accuracy measure in the target application using an extremely reduced and limited training set for learning the classification models. these results represent a relative gain of 6.2% over the best baseline ensemble method compared in this paper.

关键词： Training image segmentation Rain Planets Biological system modeling Forestry Public policy

来源：评论

学校读者我要写书评

暂无评论

Everything is image: CNN-based Short-term Electrical Load Forecasting for Smart Grid 14

Everything is Image: CNN-based Short-term Electrical Load Fo...

引用

14th International symposium on Pervasive Systems, Algorithms and Networks (I-SPAN) / 11th International Conference on Frontier of computer Science and Technology (FCST) / 3rd International symposium of Creative Computing (ISCC)

作者： Li, Liangzhi Ota, Kaoru Dong, Mianxiong Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan

ISBN: (纸本)9781538608401

Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting precision, we successfully transform the numerical prediction problem into an image processing task, and, based on that, utilize the state-of-the-art deep learning methods, which have been widely used in computer image area, to perform the electrical load forecasting. A novel deep learning based short-term forecasting (DLSF) method is proposed in the paper. Our method can perform accurate clustering on the input data using a deep Convolutional Neural Network (CNN) model. And ultimately, another neural network with three hidden-layers is used to predict the electric load, considering various external influencing factors, e.g. temperature, humidity, wind speed, etc. Experimental results demonstrate that the proposed DLSF method performs well in both accuracy and efficiency.

关键词： Load forecasting Neural networks Forecasting Support vector machines Machine learning Load modeling Training

来源：评论

学校读者我要写书评

暂无评论

Act: An easy-to-use and dynamically extensible 3D graphics library

Act: An easy-to-use and dynamically extensible 3D graphics l...

引用

Proceedings of the 1997 10th brazilian symposium of computer Graphic and image processing, SIBGRAPI 97

作者： Celes, Waldemar Corson-Rikert, Jonathan Cornell Univ Ithaca United States

this paper presents Act, an object-oriented library for projects from specialized 3D graphics applications to simple interactive educational programs. Act meets the needs of both occasional and expert graphics programmers, offering a high-level access to the OpenGL library and additional features including object creation, manipulation, and interaction. An interpreted language allows the Act library to be extended easily and dynamically.

关键词： Interactive computer graphics

来源：评论

学校读者我要写书评

暂无评论

Evaluating Interpretability in Deep Learning using Breast Cancer Histopathological images 35

Evaluating Interpretability in Deep Learning using Breast Ca...

引用

35th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Macedo, Daniel C. de Lima, John W. S. Santos, Vinicius D. Moraes, Tasso L. O. Neto, Fernando M. P. Arrais, Nicksson Vinuto, Tiago Lucena, Joao Univ Fed Pernambuco Recife Ctr Informat BR-50740560 Recife PE Brazil SiDi Ave Cais Apolo 2221 Andar Recife PE Brazil

ISBN: (数字)9781665453851

ISBN: (纸本)9781665453851

Breast cancer is the most common cancer type and is the leading cause of death among females worldwide. Despite these negative statistics, early diagnosis gives patients a high probability of survival. In literature, diagnostic techniques based on histopathological images were proposed for early diagnosis. However, they are limited because they depend on the pathologist's work and experience. In other words, a patient may receive a different diagnosis from different pathologists, or inexperienced pathologists may misdiagnose. In this work, we implement five Deep Neural Networks (DNNs) and evaluate classification accuracy and interpretability from real tumour images. To evaluate our models, we propose a metric to assess the interpretability of Deep Neural Networks (DNNs). the experiments with the BreCaHAD annotated dataset have shown that MobileNetV2 presented a higher accuracy in classifying histopathological images and interpreting their features, leading the way to improve the pathologist's work.

关键词： Deep learning Training Malignant tumors Neural networks computer architecture Transformers Breast cancer

来源：评论

学校读者我要写书评

暂无评论

On the effect of relaxation in the convergence and quality of statistical image reconstruction for emission tomography using block-iterative algorithms

On the effect of relaxation in the convergence and quality o...

引用

SIBGRAPI 2005: 18th brazilian symposium on computer graphics and image processing - Conference Proceedings

作者： Neto, Elias Salomão Helou De Pierro, Álvaro Rodolfo Universidade Estadual de Campinas Inverse Problems Group IMECC

ISBN: (纸本)0769523897

Relaxation is widely recognized as a useful tool for providing convergence in block-iterative algorithms [1], [2], [6]. In the present article we give new results on the convergence of RAMLA (Row Action Maximum Likelihood Algorithm) [2], filling some important theoretical gaps. Furthermore, because RAMLA and OS-EM (Ordered Subsets - Expectation Maximization) [4] are the algorithms for statistical reconstruction currently being used in commercial emission tomography scanners, we present a comparison between them from the viewpoint of a specific imaging task. Our experiments show the importance of relaxation to improve image quality. © 2005 IEEE.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Interactive Ground-Truth-Free image Selection for FLIM Segmentation Encoders 37

Interactive Ground-Truth-Free Image Selection for FLIM Segme...

引用

37th SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI)

作者： Cerqueira, Matheus A. Sprenger, Flavia Teixeira, Bernardo C. A. Guimaraes, Silvio Jamil E. Falcao, Alexandre X. Univ Estadual Campinas Inst Comp Campinas SP Brazil Univ Fed Parana Hosp Clin Curitiba Parana Brazil Pontificia Univ Catolica Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil

ISBN: (纸本)9798350376043;9798350376036

Deep Learning has shown impressive results in computer vision tasks at the cost of becoming increasingly extensive and requiring a voluminous amount of labeled images. Feature Learning from image Markers (FLIM) is a method to build convolutional encoders with minimal user effort in image annotation. FLIM estimates the encoder's filters directly from markers drawn by the user (scribbles, clicks) in significant regions of a few representative images (e.g., 9). However, the selection of those few images significantly impacts the model's performance, and image selection is mainly addressed by either the user inspecting all images and subjectively selecting the most relevant ones or by using a method that requires pixel-wise annotation (ground-truth) for recommending images. the main goal of our work is to make this task less subjective by presenting a ground-truth-free method for recommending representative images to train FLIM encoders while guiding the user's choice with a visual explanation. Our method suggests new images based on the distance to the previously selected images, using a proposed descriptor that generates different patterns according to filter attention. the user selects the next image among the suggested ones, which repeats until the user's satisfaction. According to our experiments, the proposed method can outperform expert manual selection and an interactive method based on pixel-wise annotation. these results are demonstrated for brain tumor and gastrointestinal parasite segmentation.

关键词： image annotation

来源：评论

学校读者我要写书评

暂无评论

Medical image processing, Analysis & Visualization in clinical research

Medical Image Processing, Analysis & Visualization in clinic...

引用

14th IEEE symposium on computer-Based Medical Systems (CBMS 2001)

作者： McAuliffe, MJ Lalonde, FM McGarry, D Gandler, W Csaky, K Trus, BL NEI CBEL Ctr Informat Technol NIH Bethesda MD 20892 USA

ISBN: (纸本)0769510043

Imaging has become an essential component in many fields of medical and laboratory research and clinical practice. Biologists study, cells and generate 3D confocal microscopy datasets, virologists generate 3D reconstructions of viruses from micrographs, radiologists identify and quantify tumors from MRI and CT scans, and neuroscientists detect regional metabolic brain activity from PET and functional MRI scans. Analysis of these diverse image types requires sophisticated computerized quantification and visualisation tools. Until recently, three-dimensional visualization of images and quantitative analysis could only be performed using expensive UNIX workstations and customized software. Today, much of the visualization and analysis can be performed on an inexpensive desktop computer equipped with the appropriate graphics hardware and software, this paper introduces all extensible platform-independent, general-purpose image processing and visualization program specifically designed to meet the needs of a lnternet-linked medical research community. the application named MIPAV (Medical image processing Analysis and Visualization) enables clinical and quantitative analysis of medical images over the Internet. Using MIPAV's standard user-interface and analysis tools, researchers and clinicians at remote sites can easily share research data and analyses, thereby enhancing their ability to study, diagnose, monitor, and treat medical disorders.

关键词： Medical imaging

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112页 << < 32 33 34 35 36 37 38 39 40 41 > >>

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：