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

8,212 篇 会议
158 篇 期刊文献
36 册 图书

馆藏范围

8,405 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,774 篇 工学
- 5,035 篇 计算机科学与技术...
- 3,597 篇 软件工程
- 1,534 篇 光学工程
- 865 篇 信息与通信工程
- 663 篇 电气工程
- 640 篇 控制科学与工程
- 501 篇 机械工程
- 449 篇 生物医学工程（可授...
- 373 篇 电子科学与技术（可...
- 349 篇 生物工程
- 237 篇 仪器科学与技术
- 119 篇 化学工程与技术
- 101 篇 建筑学
- 92 篇 土木工程
- 71 篇 安全科学与工程
- 58 篇 材料科学与工程（可...
- 51 篇 交通运输工程
3,197 篇 理学
- 1,980 篇 物理学
- 1,902 篇 数学
- 578 篇 统计学（可授理学、...
- 408 篇 生物学
- 126 篇 化学
- 57 篇 系统科学
485 篇 管理学
- 327 篇 图书情报与档案管...
- 174 篇 管理科学与工程(可...
- 55 篇 工商管理
424 篇 医学
- 407 篇 临床医学
- 105 篇 基础医学(可授医学...
- 79 篇 药学(可授医学、理...
54 篇 艺术学
- 53 篇 设计学（可授艺术学...
53 篇 农学
45 篇 法学
27 篇 教育学
18 篇 经济学
11 篇 军事学
5 篇 文学

主题

1,328 篇 image processing
1,101 篇 computer vision
894 篇 image segmentati...
663 篇 pattern recognit...
538 篇 image reconstruc...
515 篇 image analysis
501 篇 cameras
452 篇 layout
375 篇 shape
366 篇 computer science
317 篇 feature extracti...
268 篇 face recognition
264 篇 image recognitio...
260 篇 robustness
243 篇 humans
202 篇 pixel
200 篇 image edge detec...
192 篇 object recogniti...
189 篇 object detection
189 篇 pattern recognit...

机构

23 篇 department of co...
20 篇 microsoft resear...
17 篇 center for autom...
16 篇 the robotics ins...
15 篇 national laborat...
15 篇 institute of ima...
15 篇 institute of ima...
15 篇 department of co...
15 篇 institute of com...
14 篇 department of co...
14 篇 tsinghua univers...
14 篇 school of comput...
14 篇 nec research ins...
14 篇 school of comput...
13 篇 robotics institu...
13 篇 institute for ro...
12 篇 computer science...
11 篇 carnegie mellon ...
11 篇 swiss fed inst t...
11 篇 inria sophia-ant...

作者

31 篇 anon
27 篇 huang thomas s.
25 篇 jain anil k.
24 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 haralick robert ...
19 篇 timofte radu
18 篇 shum heung-yeung
18 篇 aggarwal j.k.
17 篇 zhang lei
17 篇 hancock edwin r.
16 篇 van gool luc
15 篇 g. healey
14 篇 davis larry s.
14 篇 rosenfeld azriel
14 篇 t. kanade
14 篇 r. szeliski
14 篇 ahuja narendra
13 篇 k. ikeuchi
13 篇 chellappa rama

语言

8,191 篇 英文
127 篇 其他
87 篇 中文
3 篇 土耳其文
1 篇 西班牙文
1 篇 葡萄牙文

检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是41-50 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：

A temporal-spatial feature coordination network for change detection of VHR images 3

A temporal-spatial feature coordination network for change d...

引用

3rd International conference on Geographic Information and Remote Sensing Technology, GIRST 2024

作者： Zeng, Yuxian Zhang, Xinxin College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781510689077

Deep learning techniques have significantly improved the accuracy and efficiency of change detection of very high resolution (VHR) images. However, many current models ignore the inherent heterogeneity of bi-temporal remote sensing images, thus making it difficult to distinguish between target changes and background variations. To address this problem, we propose a deep learning (DL) framework, namely temporal-spatial feature coordination network (TSFCN), to improve the accuracy of detection by enhancing the differentiation of change pixel features and improving the extraction and processing.of change information. Further comparison and ablation experiments on the SVCD dataset show that the proposed model, compared with other models, improves the F1 scores and IoU by 2.09% and 3.81%, respectively. The results demonstrate that our model can achieve better accuracy and performance improvement. © 2025 SPIE.

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

Online Extraction of Electrochemical Impedance Spectroscopy pattern Based on EV Load Profile and Short Time Fourier Transform for Diagnosis of Lithium-ion Battery Safety 14

Online Extraction of Electrochemical Impedance Spectroscopy ...

引用

14th Annual ieee Applied Power Electronics conference and Exposition, APEC 2025

作者： Lee, Miyoung Lee, Dongcheol Bae, Youngmin An, Jongchan Yang, Garam Na, Woonki Kim, Jonghoon Chungnam National University Department of Electrical Engineering Daejeon Korea Republic of California State University Department of Electrical and Computer Engineering Fresno United States

ISBN: (纸本)9798331516116

Batteries which convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurs within a battery during operation of an electric vehicle (EV) depends on environmental conditions such as temperature, load, and humidity, and these variables greatly affect battery performance and life. However, there is challenging to analyze these electrochemical phenomena with only simple electric signals, such as direct current (DC) signal. For compensation, passive electrochemical impedance spectroscopy (PEIS) is derived by applying a signal processing.algorithm that converts time series signals into frequency-domain signals in this study. Based on this, we propose to learn a siamese convolutional neural network (SCNN) algorithm on the converted PEIS pattern to the image and to effectively diagnose the state of the battery through similarity evaluation. © 2025 ieee.

关键词： Network coding

来源：评论

学校读者我要写书评

暂无评论

Foreground Prediction for image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

引用

2024 16th International conference on Graphics and image processing. ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Sparse bidirectional two-dimensional principal component analysis for feature extraction

Sparse bidirectional two-dimensional principal component ana...

引用

2024 International conference on Measurement, Communication, and Virtual Reality, MCVR 2024

作者： Liu, Wenwen Kang, Xinyuan Feng, Weijia College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China

ISBN: (纸本)9781510691773

The use of computer vision technology to achieve recognition has become an important research topic in the field of artificial intelligence, and feature extraction is an important step in automatic recognition using computer vision technology. The spatio-temporal feature descriptor extracts a feature vector with excessively high dimensionality, which increases the computational complexity of various classification tasks and extends the processing.time. PCA aims to map high dimensional data to low-dimensional space through linear transformation and is widely used in the fields of dimensionality reduction and feature extraction, data compression, and pattern recognition. However, PCA has shortcomings, such as losing the inherent two-dimensional features of the image and producing principal components that are unfavorable for interpretation. To address these issues, this paper proposes a sparse bidirectional two-dimensional principal component analysis algorithm (S2D2PCA). This algorithm is developed by applying sparse constraints to the principal components of the bidirectional two-dimensional principal component analysis algorithm (2D2PCA). The S2D2PCA algorithm extracts features directly from two-dimensional images and preserves their two-dimensional features;improves the interpretability of the principal components, reduces redundancy, and improves the efficiency of feature extraction. In this paper, experimental validation is conducted on the ORL and Weld datasets. The results demonstrate that the classification performance of the S2D2PCA algorithm is superior to that of PCA and 2D2PCA. © 2025 SPIE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

ieee Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： G.V. Thanuja Ramya S Dept. Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing.power provided by the embedded processor. Integrating image processing.into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing.through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications.

关键词： image edge detection Computed tomography Software algorithms Signal processing.algorithms computer architecture Table lookup IP networks Logic Field programmable gate arrays Kidney

来源：评论

学校读者我要写书评

暂无评论

Dual-channel capsule shape detection system based on computer vision and optical imaging technologies 5

Dual-channel capsule shape detection system based on compute...

引用

5th International conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Zhang, Tinghao University of Nottingham University Park Nottingham NG7 2RD United Kingdom

ISBN: (纸本)9781510691667

With the rapid advancement of industrial automation and smart manufacturing, computer vision technology, enhanced by optical imaging techniques, has found extensive application in the pharmaceutical industry, particularly in capsule shape detection. The integrity of capsule appearance directly impacts drug efficacy, and optical imaging plays a critical role in capturing precise details of capsule shapes, ensuring enhanced detection accuracy. Traditional manual inspection methods are limited by low efficiency, poor reliability, and high costs, making them unsuitable for large-scale, high-precision production. Automated detection systems based on optical sensors, coupled with computer vision, enable real-time monitoring and defect detection through image acquisition, processing. and pattern recognition, significantly improving detection speed and accuracy while reducing human error. This paper proposes a dual-channel capsule shape detection system that utilizes high-resolution optical cameras for image acquisition and incorporates an optimized deep learning algorithm to improve detection efficiency and accuracy. The system integrates advanced optical imaging techniques, such as precise lighting and edge detection, to enhance feature extraction capabilities for complex capsule images. Experimental results show that the dual-channel system significantly improves detection accuracy, achieving over 98.5% accuracy and reducing false detection rates, while enhancing the speed and reliability of capsule inspection in high-speed production environments. The combination of optical imaging and deep learning makes this system a promising solution for quality control in the pharmaceutical industry, supporting the transition to intelligent manufacturing and flexible production. © 2025 SPIE.

关键词： capsule shape detection computer vision dual-channel detection optical imaging real-time detection system

来源：评论

学校读者我要写书评

暂无评论

8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

引用

8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

The proceedings contain 158 papers. The special focus in this conference is on Life System Modeling and Simulation and Intelligent Computing for Sustainable Energy and Environment. The topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature processing.and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-Thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

关键词：

来源：评论

学校读者我要写书评

暂无评论

8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

引用

8th International conference on Life System Modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602933

关键词：

来源：评论

学校读者我要写书评