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

15,969 篇 会议
373 篇 期刊文献
193 册 图书

馆藏范围

16,535 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,382 篇 工学
- 5,460 篇 计算机科学与技术...
- 3,009 篇 软件工程
- 2,171 篇 电气工程
- 1,880 篇 信息与通信工程
- 1,137 篇 控制科学与工程
- 897 篇 光学工程
- 650 篇 机械工程
- 643 篇 电子科学与技术（可...
- 542 篇 仪器科学与技术
- 540 篇 生物医学工程（可授...
- 475 篇 生物工程
- 229 篇 测绘科学与技术
- 221 篇 交通运输工程
- 204 篇 航空宇航科学与技...
- 203 篇 材料科学与工程（可...
- 164 篇 化学工程与技术
- 160 篇 网络空间安全
- 140 篇 安全科学与工程
- 138 篇 土木工程
4,043 篇 理学
- 2,416 篇 物理学
- 1,373 篇 数学
- 527 篇 生物学
- 369 篇 统计学（可授理学、...
- 340 篇 系统科学
- 210 篇 化学
1,525 篇 医学
- 1,352 篇 临床医学
- 204 篇 特种医学
- 196 篇 基础医学(可授医学...
1,056 篇 管理学
- 614 篇 管理科学与工程(可...
- 482 篇 图书情报与档案管...
83 篇 法学
68 篇 农学
44 篇 经济学
44 篇 教育学
37 篇 军事学
32 篇 文学
25 篇 艺术学
1 篇 哲学

主题

2,803 篇 image processing
1,563 篇 image segmentati...
1,513 篇 signal processin...
877 篇 feature extracti...
658 篇 image edge detec...
636 篇 cameras
632 篇 real-time system...
631 篇 signal processin...
582 篇 image reconstruc...
519 篇 image coding
498 篇 algorithm design...
411 篇 deep learning
400 篇 image color anal...
399 篇 clustering algor...
386 篇 object detection
374 篇 image enhancemen...
373 篇 accuracy
337 篇 hardware
327 篇 image analysis
317 篇 computer vision

机构

40 篇 xi'an jiaotong u...
32 篇 xi an jiao tong ...
32 篇 northwestern pol...
29 篇 school of electr...
27 篇 school of electr...
26 篇 university of ch...
24 篇 xi an jiao tong ...
22 篇 xi an jiao tong ...
22 篇 national laborat...
21 篇 signal and image...
19 篇 samara national ...
18 篇 xidian universit...
17 篇 school of inform...
16 篇 institute of art...
15 篇 tsinghua univers...
15 篇 chang'an univers...
15 篇 department of el...
15 篇 school of automa...
15 篇 national key lab...
14 篇 school of electr...

作者

39 篇 chen xi
29 篇 xi chen
24 篇 nanning zheng
24 篇 licheng jiao
20 篇 li xi
20 篇 liu ying
17 篇 anon
17 篇 xuelong li
14 篇 xi ning
14 篇 wei li
14 篇 yuan yuan
13 篇 de-shuang huang
13 篇 yanning zhang
13 篇 xi li
12 篇 jie yang
12 篇 yang xi
11 篇 p. pirsch
11 篇 liu yang
11 篇 mingyi he
11 篇 ying liu

语言

15,467 篇 英文
925 篇 其他
156 篇 中文
19 篇 土耳其文
3 篇 葡萄牙文
2 篇 法文
2 篇 俄文
1 篇 西班牙文

检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16535 条记录，以下是1221-1230 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：

Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection 4

Enhanced YOLOv8 for rapid diagnosis and analysis of infrared...

引用

4th International conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Zhou, Ming Wu, Qiang xiong, Wei Ma, Li Cheng, Zhao Sun, Liping State Grid Hubei Electric Power Company Wuhan China Economic and Technological Research Institute State Grid Hubei Electric Power Company Wuhan China

ISBN: (纸本)9781510686106

Insulator defects in power transmission systems pose significant risks to grid stability and safety. This paper presents an improved method for rapid diagnosis of insulator defects using infrared images, based on an enhanced YOLOv8 algorithm. Our approach incorporates Haar wavelet downsampling and a Polarized Self-Attention (PSA) mechanism to boost detection accuracy while maintaining real-time performance. The Haar wavelet downsampling Boosts the model's capability to grasp multi-scale features, concurrently enhancing the PSA mechanism's emphasis on pertinent thermal patterns. We evaluate our method on a comprehensive dataset of 10,000 infrared images collected from various power transmission environments. Experimental results demonstrate that our improved YOLOv8 model achieves SOTA, with a mean Average Precision of 94.7%, outperforming the original YOLOv8 and other object detection algorithms. The proposed method offers a 3.2% increase in mAP compared to the original YOLOv8, while maintaining a processing speed of 42.8 frames per second. This research contributes significantly to the advancement of more efficient and precise outcomes power grid maintenance techniques, potentially reducing the need for manual inspections and enhancing overall system reliability. © 2024 SPIE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Online Microscopic Imaging Combined with Machine Learning image processing for Online Monitoring of Mineral Froth Flotation Process

Online Microscopic Imaging Combined with Machine Learning Im...

引用

2024 8th International conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： xianfa Dong Xuezhong Wang College of New Materials and Chemical Engineering Beijing Institute of Petrochemical Technology Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Froth flotation is an important process in the mineral processing industry for extracting valuable materials. This work investigates online microscopic imaging and machine learning based image analysis methods for real-time monitoring of the process. Previous limited work explored imaging the foam at the top surface layer of the froth flotation process. The new process imaging system in this work uses a corrosion-resistant online real-time imaging probe that can be put into the inside of the slurry and capture real-time images of bubbles. The acquired images are analyzed online using deep learning algorithms to automatically obtain key parameters of the bubbles, providing valuable data for froth flotation research and process control.

关键词： Manufacturing processes Microscopy Process control Real-time systems Minerals Materials processing Probes Surface treatment Slurries Monitoring

来源：评论

学校读者我要写书评

暂无评论

RNCE: A New image Segmentation Approach

RNCE: A New Image Segmentation Approach

引用

International conference on Computer Vision and Machine Intelligence, CVMI 2022

作者： Kumar, Vikash Ali, Asfak Chaudhuri, Sheli Sinha Electronics and Telecommunication Engineering West Bengal Jadavpur Kolkata700032 India

ISBN: (纸本)9789811978661

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://***/vekash2021/***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Improved ShuffleNetV2 for Garbage image Classification 18th

Improved ShuffleNetV2 for Garbage Image Classification

引用

18th International conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Zhang, Jinghan Li, Cheng Guo, Baolong Institute of Intelligent Control and Image Engineering Xidian University Xi’an China

ISBN: (纸本)9789819906048

This paper proposes a novel lightweight classification network based on ShuffleNet V2 to promote the accuracy of garbage image classification. Compared with the conventional network, there is a package of optimizations to both ameliorate the classification accuracy and inference speed of the model. Firstly, the optimized network introduces the Leaky ReLU as a substitute for the activation function. It also adopts the squeeze and excitation network to enhance important features. In addition, the flattened layer is applied to downscale the data, and three fully connected layers are used for enhancing the learning capability. Furthermore, the model is pre-trained based on transfer learning, wherein the feature knowledge learned from imageNet datasets is transferred to garbage classification task. Extensive experiments on the garbage image dataset verify the overall performance, which improves the classification accuracy by 6.3% with a 35.4% reduction in time consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Determining image Deblur, Detecting Faces, and Enhancing Appearance Using image processing Techniques

Determining Image Deblur, Detecting Faces, and Enhancing App...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Norazliani Md Sapari Khairul Huda Yusof Nicholas Tan Kien Lung Madihah Md Rasid Siti Maherah Hussin Centre of Electrical Energy Systems (CEES) Faculty of Electrical Engineering UTM Skudai Johor Faculty of Information Science & Engineering Management & Science University Selangor Malaysia

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

This paper explores the utilization of MATLAB for digital signal processing (DSP) techniques in image processing tasks, focusing on image deblurring, face detection, and facial feature enhancement. Blind deconvolution methods are employed to address image blurriness, while face detection is facilitated using cascaded object detectors. Enhancements to detected facial features involve histogram equalization, smoothing filters, skin tone adjustment, and contrast enhancement techniques, followed by seamless integration using resizing methods. MATLAB serves as a robust platform for implementing and analyzing DSP algorithms, providing insights into practical solutions for common challenges in digital image processing.

关键词： Visualization Histograms Digital images Signal processing algorithms Digital signal processing Skin image restoration

来源：评论

学校读者我要写书评

暂无评论

An Efficient Workload Distribution Mechanism for Tightly Coupled Heterogeneous Hardware 6th

An Efficient Workload Distribution Mechanism for Tightly Co...

引用

6th World conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Rivera-Alvarado, Ernesto Torres-Rojas, Francisco J. Computer Science Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (纸本)9789811976599

The Accelerated processing Units (APUs) are tightly integrated heterogeneous computing platforms that contain a CPU and GPU in a single integrated circuit. As their internal computing resources use the same memory (RAM), they can efficiently share data structures. This opens up the opportunity for creating new types of specialized algorithms for the APU. In this research, we designed a specialized load distribution mechanism for the APU and used image rendering as our test load as it is a highly expensive computational task. We found out that our workload distribution mechanism provides better performance than conventional workload mechanisms for GPUs and multicore CPUs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： APU Computer architecture Heterogeneous computing image rendering SoC System-on-chip Tightly coupled heterogeneous hardware Workload distribution

来源：评论

学校读者我要写书评

暂无评论

Real-time image Enhancement for Emergency Rescue Scenarios in Smart Grids

Real-time Image Enhancement for Emergency Rescue Scenarios i...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE International conference on

作者： Zeng Dou Jingyi Tao Ning Lv Li Cong Chengbin Huang Tao Su State Grid Jilin Province Electric Power Company Limited Information Communication Company Changchun China School of Electronic Engineering Xidian Hangzhou Institute of Technology Xidian University Hangzhou China School of Electronic Engineering Xidian University Xi’an China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

image enhancement technology plays an important role in the practical application of detecting underwater tunnels. By improving image quality, it enhances the accuracy and reliability of detection results. However, in deep-sea scenes, the problem of image color distortion and detail loss is common. To address this issue, a real-time underwater image enhancement algorithm based on weighted multi-branch correction has been proposed in this paper. The algorithm uses a combination of multi-scale and multi-branch techniques to process the image. The multi-branch correction module has been improved by increasing the weighting and downsampling strategy, which not only improves the processing speed but also ensures the processing effect. In the air-frequency domain interactive processing module, zero padding and window functions have been introduced to reduce the ringing effect and block effect. In addition, the smart grid system provides stable power support for underwater tunnel search and rescue, which ensures the safety of the search and rescue environment and also provides a good experimental lighting environment for the image enhancement algorithm. At the same time, the model has been trained and validated using self-constructed real deep-sea datasets. The existing algorithms and the algorithm proposed in this paper have been compared and analyzed comprehensively. The experimental results show that the algorithm proposed in this paper is better than the existing algorithms in terms of processing effect, and the processing speed is improved by about 12.7%.

关键词： image quality image color analysis Lighting Information filters Real-time systems Smart grids Safety

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

引用

4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Joint MLP and Pseudo-label Unsupervised Hash for image Retrieval 5

Joint MLP and Pseudo-label Unsupervised Hash for Image Retri...

引用

5th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Chao, Lu Liu, Ying Yu, Hao Yang, Jianning Li, Daxiang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

The increasing growth of image data has made the implementation of image retrieval in big data environment a hot research topic. Traditional hashing technique uses hand-designed features for learning, which can't obtain high-level semantic features. Deep hashing has become a powerful tool for solving multimedia data retrieval in recent years with the powerful feature learning capability and the inherent advantage of small hash code storage/fast retrieval. Most of these algorithms rely on manual tagging, which is very expensive. In this paper, we propose the Joint MLP and Pseudo-label unsupervised hash (JMPH). In this approach, object features in the image are extracted with the target detection method as pseudo-labels to train the deep hash. In particular, we additionally devise the MLP network to conduct the encoding process. Experimental results on two datasets demonstrate that our unsupervised hashing method outperforms the state-of-art unsupervised hashing methods. © 2022 ACM.

关键词： image retrieval

来源：评论

学校读者我要写书评

暂无评论

Automatic Classification and Recognition of Tourist Attraction images using k-Means Algorithm and image processing Technology

Automatic Classification and Recognition of Tourist Attracti...

引用

Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Qian Jiang School of Public Administration Chongqing Vocational College of Transportation China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study investigates the automatic classification and recognition of tourist attraction images through an enhanced K-means algorithm paired with image processing techniques. Given the growing interest in efficient tourist image management and promotion, our research presents a system that significantly advances the processing and categorization of scenic spot images. We implemented a variety of image preprocessing steps—such as grayscale conversion, normalization, Gaussian filtering, and edge detection — to improve the accuracy of feature extraction. Our method involved an optimized K-means algorithm, modified to select initial centroids more effectively and adjust cluster numbers dynamically. Experimental results, based on a dataset of 150 images, demonstrated a classification accuracy of 92%, with the system maintaining robust performance even as dataset sizes increased, confirming the algorithm's efficiency and stability. The processing time for 5000 images was recorded at 12.3 seconds, showcasing a linear relationship between data size and processing time. These findings highlight the potential of our proposed system in enhancing tourism management and marketing strategies by providing a reliable and scalable image classification solution.

关键词： Accuracy Filtering image processing image edge detection Heuristic algorithms Clustering algorithms Feature extraction Classification algorithms image preprocessing Reliability

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500页 << < 119 120 121 122 123 124 125 126 127 128 > >>

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：