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

12,550 篇 会议
192 篇 期刊文献
161 册 图书

馆藏范围

12,903 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

6,493 篇 工学
- 4,276 篇 计算机科学与技术...
- 2,182 篇 软件工程
- 1,870 篇 电气工程
- 1,278 篇 信息与通信工程
- 824 篇 控制科学与工程
- 652 篇 光学工程
- 462 篇 生物医学工程（可授...
- 438 篇 电子科学与技术（可...
- 434 篇 机械工程
- 424 篇 仪器科学与技术
- 316 篇 生物工程
- 220 篇 测绘科学与技术
- 151 篇 材料科学与工程（可...
- 142 篇 航空宇航科学与技...
- 133 篇 交通运输工程
- 99 篇 安全科学与工程
- 86 篇 网络空间安全
3,122 篇 理学
- 2,082 篇 物理学
- 887 篇 数学
- 359 篇 生物学
- 246 篇 系统科学
- 224 篇 统计学（可授理学、...
- 108 篇 化学
1,429 篇 医学
- 1,268 篇 临床医学
- 192 篇 特种医学
- 158 篇 基础医学(可授医学...
- 99 篇 药学(可授医学、理...
690 篇 管理学
- 421 篇 管理科学与工程(可...
- 291 篇 图书情报与档案管...
- 84 篇 工商管理
58 篇 法学
57 篇 农学
30 篇 教育学
27 篇 文学
25 篇 经济学
17 篇 军事学
16 篇 艺术学
1 篇 哲学

主题

2,645 篇 image processing
1,386 篇 image segmentati...
1,142 篇 signal processin...
610 篇 cameras
595 篇 feature extracti...
582 篇 image edge detec...
521 篇 signal processin...
472 篇 image reconstruc...
462 篇 image coding
460 篇 real-time system...
390 篇 algorithm design...
334 篇 image color anal...
307 篇 deep learning
306 篇 image enhancemen...
306 篇 pixel
300 篇 real time system...
293 篇 object detection
290 篇 computer archite...
283 篇 neural networks
283 篇 hardware

机构

22 篇 signal and image...
19 篇 samara national ...
14 篇 school of electr...
11 篇 samara national ...
11 篇 univ texas san a...
11 篇 department of el...
11 篇 school of electr...
10 篇 center for signa...
9 篇 information syst...
9 篇 institute of ima...
9 篇 tongji universit...
8 篇 university of to...
8 篇 purdue univ sch ...
8 篇 university of ch...
8 篇 electrical compu...
8 篇 image processing...
8 篇 information syst...
8 篇 samara national ...
7 篇 school of electr...
7 篇 department of ma...

作者

19 篇 anon
13 篇 de-shuang huang
13 篇 jagdish chand ba...
11 篇 p. pirsch
10 篇 jie yang
10 篇 panetta karen
10 篇 j.m. mendel
10 篇 r. chellappa
10 篇 a. ortega
9 篇 c.-c.j. kuo
8 篇 frank hannig
8 篇 bovik alan c.
8 篇 hannig frank
8 篇 mahalanobis a
8 篇 ilias maglogiann...
8 篇 carli m.
7 篇 miller jwv
7 篇 busch christoph
7 篇 semenishchev e.
7 篇 zelensky a.

语言

12,608 篇 英文
209 篇 其他
74 篇 中文
20 篇 土耳其文
3 篇 葡萄牙文
2 篇 西班牙文
2 篇 俄文
1 篇 法文

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

共 12903 条记录，以下是801-810 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：

Smart Traffic Light Control System Using image processing

Smart Traffic Light Control System Using Image Processing

引用

2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Banu, Asha S.M Lakchida, Soundarya A S Shanthini, V.S. Stinsha, S.L. Sri Krishna College of Engineering and Technology Dept of ECE India

ISBN: (纸本)9781665489621

Crossing the road is one of the major problems, due to the increase of fast-moving vehicles on the road. The challenges in the existing techniques utilized for traffic control can be overcome by using the Smart Traffic Light Control System using image processing, proposed in this paper and allowing pedestrians to walk on busy roads conveniently. It is found that the Canny Edge Detection Technique is a very effective method among the various edge detection algorithms. The suggested method reduces the waiting time in zebra crossing by immediately controlling the traffic light and gives more time for elderly and handicapped pedestrians to cross the roads safely even during heavy traffic. When compared to all the traditional techniques, image processing is an efficient method for traffic control. This technique removes the usage of unwanted hardware like sensors which are used to sense noises. This prevents the wastage of time for vehicles which is more essential for emergency vehicles. The output of the code is obtained by image matching. The results are shown in three scenarios: less traffic, moderate traffic, and more traffic. © 2022 IEEE.

关键词： Roads and streets

来源：评论

学校读者我要写书评

暂无评论

A Dual-Approach to Drone image Enhancement: Software-Based processing and FPGA Implementation

A Dual-Approach to Drone Image Enhancement: Software-Based P...

引用

Smart Electronics and Communication systems (ISENSE), International conference on

作者： Arun T Nair Nanditha N Varma Nandakumar R National Institute of Electronics and Information Technology Calicut

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is the main goal since it is essential for applications like environmental monitoring, navigation, and surveillance. In order to estimate the atmospheric light and transmission map, the suggested methodology makes use of the dark channel. A farrow filter and a guided filter are then applied to improve the transmission. Metrics including the PSNR, SSIM, Execution time, and MSE that are derived from Python-based image processing implementations are used to assess the effectiveness of the dehazing algorithms. The outcomes show notable gains in processing efficiency and image clarity. In order to accelerate the performance, this method was implemented on an FPGA based SoC. Power consumption and throughput of the FPGA - based solution were evaluated, demonstrating its effectiveness and appropriateness for real-time applications.

关键词： Surveillance Video sequences Random access memory Gray-scale Filtering algorithms Throughput Real-time systems System-on-chip Field programmable gate arrays Tuning

来源：评论

学校读者我要写书评

暂无评论

Automatic conversion system of digital music score image based on image recognition algorithm 3

Automatic conversion system of digital music score image bas...

引用

3rd International conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Yao, Yi Xi'an Conservatory of Music Shaanxi Xi'an710000 China

ISBN: (纸本)9781510661240

The development of target detection and recognition algorithms in the field of image processing has promoted the development of automatic image conversion systems for digital musical scores and related algorithms. This was crucial for the development of note-recognition coding and the conversion of musical scores into readable digital formats. The purpose of this paper is to study the automatic conversion system of digital music score image based on image recognition algorithm. The deep learning technology based on computer vision is used for musical score image recognition, and a digital musical score image automatic conversion system is designed and built. Harmonic peak method and confidence method are introduced, and their shortcomings are analyzed. Combined with the advantages of these two algorithms, an improved normal harmonic method is proposed, and the detailed ideas and steps of the algorithm are given. And the extracted fundamental frequency and time information are converted into pitch and time value combined with the basic knowledge of music theory. The recognition accuracy rate of the normal harmonic method proposed in this paper reaches more than 92%, which not only greatly improves the recognition accuracy, but also retains the strong anti-noise characteristics of the traditional harmonic peak method. © 2022 SPIE.

关键词： image recognition

来源：评论

学校读者我要写书评

暂无评论

Application Of MLOps Practices For Biomedical image Classification 5

Application Of MLOps Practices For Biomedical Image Classifi...

引用

5th International conference on Informatics and Data-Driven Medicine (IDDM)

作者： Berezsky, Oleh Pitsun, Oleh Melnyk, Grygory Batko, Yuriy Derysh, Bohdan Liashchynskyi, Petro West Ukrainian Natl Univ 11 Lvivska St UA-46001 Ternopol Ukraine

With active hardware development, the number of software machine learning-based systems has increased dramatically in all areas of human activity, in particular, in medicine. The use of machine learning elements in software systems requires the organization of a pipeline process of software development, testing, and support. The application of MLOps technologies will improve the quality and speed of system development, as well as simplify the process of adjusting the algorithm parameters to improve the system operation quality. The purpose of this work is to develop an MLOps pipeline that will consider all the necessary stages of software development based on machine learning algorithms for biomedical image processing.

关键词： Machine learning MLOps biomedical images programming

来源：评论

学校读者我要写书评

暂无评论

Retinal image Segmentation U sing Clustering Methods: Performance Analysis 19

Retinal Image Segmentation U sing Clustering Methods: Perfor...

引用

19th IEEE International Multi-conference on systems, Signals and Devices, SSD 2022

作者： Mehidi, Imane Jabri, Dalel Belkhiat, Djamel Eddine Chouaib Ferhat Abbas University Dac Hr Laboratory Setif Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Clustering is a popular method for seg-menting retinal images due to its effectiveness in performance. This paper investigates the ability of multiple clustering algorithms to segment the retinal image to isolate blood vessels. The flowchart of this analyzes two phases. First, the pre-processing phase aims to increase the contrast of the image by applying a series of operations: Contrast Limited Adaptive Histogram Equalization (CLAHE), median filtering, and bottom-hat filtering. Furthermore, the second phase deals with the segmentation of the pre-processed image using one of the clustering algorithms selected for this study: k-means (KM), k-medoids (KD), Gaussian Mixture Model (GMM), and fuzzy c-means (FCM). The experimental part is carried out on the DRIVE and STARE databases, and the performance measure of each algorithm is accomplished using sensitivity, specificity, and accu-racy performance metrics. In addition, the running times of different algorithms are compared. Finally, the obtained results are extensively discussed, and some suggestions are proposed at the end of this paper. © 2022 IEEE.

关键词： Blood vessels

来源：评论

学校读者我要写书评

暂无评论

Development of an Urban Vegetation Monitoring System using image processing and Deep Learning 10

Development of an Urban Vegetation Monitoring System using I...

引用

10th IEEE International conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Alighaleh, Pejman Ooi, Melanie Kaung, Ye Chow University of Waikato School of Engineering Hamilton New Zealand

ISBN: (纸本)9798350365283

Automated monitoring of urban vegetation, particularly trees, facilitates large-scale urban planning and environmental surveillance. However, such systems can be cost-prohibitive to deploy, especially in smaller cities. This paper describes a cost-efficient standalone RGB-imaging platform with adaptable mounting for service vehicles, implemented in Hamilton East, Hamilton City, New Zealand. First, the hardware design and imaging platform are described. Then, to evaluate the quality of the tree imagery captured by this system, two deep-learning models, YOLOv5n and YOLOv8n, were trained using the collected images. The collected dataset consists of approximately 12,000 images of various tree species typical to the region. Of these, 450 images (4%) were used for training and validation, and the remaining images were used for testing. The models were then tested on images collected in 4 different test image collection sessions. The results of the models in the validation phase showed that the YOLOv5n model demonstrated a precision of 0.87 and a recall of 0.85, compared to YOLOv8n's precision of 0.86 and recall of 0.82. During the test phase, both models performed comparably, indicating that the imaging system can capture high-quality images before direct use with deep-learning algorithms for tree detection. Furthermore, the imaging system is capable of capturing at least 11 images per tree on an urban route, showing the potential to be deployed for urban vegetation monitoring. © 2024 IEEE.

关键词： Imaging systems

来源：评论

学校读者我要写书评

暂无评论

Secure and Decentralized Collaboration in Oncology: A Blockchain Approach to Tumor Segmentation 48

Secure and Decentralized Collaboration in Oncology: A Blockc...

引用

48th Annual IEEE International Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Ranjbarzadeh, Ramin Keles, Ayse Crane, Martin Anari, Shokofeh Bendechache, Malika Dublin City Univ Fac Engn & Comp Sch Comp Dublin Ireland Ankara Medipol Univ Fac Engn & Nat Sci Dept Artificial Intelligence Engn Ankara Turkiye Islamic Azad Univ South Tehran Branch Dept Accounting Econ & Financial Sci Tehran Iran Univ Galway ADAPT Res Ctr Sch Comp Sci Galway Ireland

ISBN: (纸本)9798350376975;9798350376968

This research presents an innovative framework that uses blockchain technology to improve tumor segmentation in medical imaging. The approach tackles issues related to data security, particularly when dealing with real private dataset, annotation accuracy, and collaboration. With the growing reliance of the medical industry on accurate tumor segmentation from medical images for cancer diagnosis and treatment, current methods are inadequate in maintaining data accuracy and promoting collaboration among experts across different countries. Our suggested approach utilizes blockchain technology to establish a decentralized, secure platform for the collaborative obtaining, annotation, and validation of medical images by data scientists, oncologists, and radiologists. Smart contracts streamline essential procedures such as verification of annotations, consensus among experts, and remuneration of contributors, guaranteeing the dependability and excellence of the data. Furthermore, the unchangeable record of transactions in the blockchain ensures a reliable basis for implementing artificial intelligence and machine learning algorithms. This improves the accuracy of segmenting data and allows for predictive modeling. This strategy not only improves the precision and effectiveness of tumor segmentation but also promotes a worldwide collaborative environment, which has the potential to revolutionize cancer diagnostics and treatment planning. Furthermore, it ensures the privacy and security of patient data.

关键词： Blockchain Technology Decentralized Healthcare systems Collaborative Annotation Medical image processing Data Security and Privacy

来源：评论

学校读者我要写书评

暂无评论

Human Detection using ROI of an image

Human Detection using ROI of an Image

引用

2024 International conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Kartik, R. Megadarshini, V. Kavisri, S. Manimegalai, R. PSG Institute of Technology and Applied Research Department of EEE Tamilnadu India

ISBN: (纸本)9798350378177

Efficient human presence detection within specified Regions of Interest (ROIs) is essential to many real-world applications, including as resource allocation, security surveillance, and crowd monitoring. In this paper, we present a robust human pres-ence detection system leveraging advanced techniques, notably YOLOv8, for accurate and real-time object detection. Our system addresses the challenges associated with traditional methods by focusing on multi-scale detection windows and efficient merging of nearby detections to refine results. We introduce a streamlined approach for ROI definition and utilize transfer learning to adapt YOLOv8 to specific human presence detection tasks. Extensive experiments on diverse datasets demonstrate the effectiveness and robustness of our system, showcasing superior performance in terms of accuracy, real-time processing, and adaptability to chal-lenging scenarios. Additionally, we implemented four algorithms, including Histogram of Oriented Gradients (HOG) [1], Roboflow 3.0 object detection algorithm, YOLOv5, and YOLOv8. After thorough examination, we determined that YOLOv8 showed the most favorable outcomes in terms of both precision and speed. Hence, we opted for YOLOv8 as the primary algorithm for our system aimed at detecting human presence. This decision was informed by its superior performance across diverse datasets and its capability to address the challenges associated with traditional methods. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Synthetic Aperture Radar Signal processing Algorithm Implementation in Python 5

Synthetic Aperture Radar Signal Processing Algorithm Impleme...

引用

5th IEEE Asia-Pacific conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Setiadi, Bambang Arisal, Andria Firmansyah, Iman Subekti, Agus Chasanah, Nurul Hamonangan, Jefri Abner Kurniawan, Farohaji Research Center for Data and Information Sciences National Research and Innovation Agendy Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Research Center for Aeronautics Technology National Research and Innovation Agency Bogor Indonesia

ISBN: (纸本)9798350398212

The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks. © 2022 IEEE.

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Quilt-1M: One Million image-Text Pairs for Histopathology 37

Quilt-1M: One Million Image-Text Pairs for Histopathology

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Ikezogwo, Wisdom O. Seyfioglu, Mehmet S. Ghezloo, Fatemeh Geva, Dylan Mohammed, Fatwir S. Anand, Pavan K. Krishna, Ranjay Shapiro, Linda G. University of Washington United States

ISBN: (纸本)9781713899921

Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has slowed comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1, 087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of 802, 144 image and text pairs. QUILT was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: QUILT-1M, with 1M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal retrieval tasks. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Large datasets

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 << < 77 78 79 80 81 82 83 84 85 86 > >>

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：