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

2,973 篇 会议
261 册 图书
58 篇 期刊文献
1 篇 学位论文

馆藏范围

3,293 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,988 篇 工学
- 1,553 篇 计算机科学与技术...
- 772 篇 软件工程
- 499 篇 电气工程
- 402 篇 光学工程
- 288 篇 信息与通信工程
- 263 篇 控制科学与工程
- 170 篇 机械工程
- 134 篇 生物工程
- 119 篇 生物医学工程（可授...
- 118 篇 电子科学与技术（可...
- 93 篇 仪器科学与技术
- 65 篇 材料科学与工程（可...
- 52 篇 安全科学与工程
- 45 篇 化学工程与技术
- 45 篇 交通运输工程
- 22 篇 建筑学
- 22 篇 土木工程
1,038 篇 理学
- 828 篇 物理学
- 233 篇 数学
- 146 篇 生物学
- 62 篇 统计学（可授理学、...
- 42 篇 化学
- 22 篇 系统科学
428 篇 医学
- 416 篇 临床医学
- 44 篇 基础医学(可授医学...
- 30 篇 药学(可授医学、理...
232 篇 管理学
- 162 篇 管理科学与工程(可...
- 76 篇 图书情报与档案管...
- 24 篇 工商管理
34 篇 农学
- 31 篇 作物学
21 篇 法学
12 篇 经济学
8 篇 教育学
6 篇 军事学
6 篇 艺术学
5 篇 文学

主题

589 篇 machine vision
558 篇 image processing
531 篇 computer vision
243 篇 machine learning
204 篇 image segmentati...
197 篇 feature extracti...
192 篇 artificial intel...
158 篇 image processing...
150 篇 pattern recognit...
143 篇 deep learning
133 篇 cameras
90 篇 training
89 篇 computational mo...
84 篇 object detection
84 篇 visualization
84 篇 image recognitio...
82 篇 image analysis
82 篇 inspection
79 篇 information syst...
79 篇 image edge detec...

机构

10 篇 center for machi...
9 篇 tongji universit...
7 篇 chongqing univer...
7 篇 hefei university...
7 篇 south china norm...
7 篇 university of ox...
7 篇 harbin institute...
7 篇 univ minnesota c...
6 篇 department of co...
6 篇 moscow state uni...
6 篇 university of no...
6 篇 dipartimento di ...
6 篇 dipartimento di ...
6 篇 graz university ...
6 篇 computational sc...
6 篇 don state tech u...
6 篇 beijing institut...
6 篇 center for machi...
6 篇 dipartimento di ...
6 篇 university of fr...

作者

18 篇 de-shuang huang
13 篇 waltz fm
10 篇 ngoc thanh nguye...
10 篇 abdenour hadid
9 篇 stan sclaroff
9 篇 batchelor bg
9 篇 voronin v.
8 篇 semenishchev e.
7 篇 andrea vedaldi
7 篇 meriaudeau f
7 篇 alberto del bimb...
6 篇 haijun zhang
6 篇 miller jwv
6 篇 ikenaga takeshi
6 篇 tianyong hao
6 篇 jan-michael frah...
6 篇 zelensky a.
6 篇 thomas brox
6 篇 beyerer juergen
6 篇 hugo jair escala...

语言

3,208 篇 英文
49 篇 其他
36 篇 中文
7 篇 土耳其文
1 篇 俄文

检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

共 3293 条记录，以下是71-80 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：

Artificial Intelligence-Powered Internet of Things for Farming and Plant Research

Artificial Intelligence-Powered Internet of Things for Farmi...

引用

Electronics and Renewable Systems (ICEARS), International conference on

作者： A. Padma P. Suresh G. Narsimlu M Amarnath B. R Sreedhar Department of Mathematics Chaitanya Bharathi Institute of Technology

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The wearable fiber optic sensors for plants to monitor microenvironment and growth wearable fiber optic plant sensors for microclimate and growth monitoring. The plant agricultural sectors are facing challenges because to population expansion and climate ***-powered computer vision-based plant growth monitoring system the goal of this research project is to use computer vision to create an AI-based plant growth monitoring system. The technology will allow for automatic and real-time evaluation of plant growth and health by utilizing computer vision algorithms and artificial intelligence approaches. Conventional techniques for tracking plant growth require a lot of work and time. By employing image processing techniques to analyze plant photos and extract pertinent features leaf area, plant height, and width, the suggested solution will overcome these drawbacks. Using the collected information, machine learning methods will be used to train models that can identify patterns and forecast plant development. The system will be made to adapt to various plant species and environmental circumstances. This AI-based plant growth monitoring system will help farmers, researchers, and plant enthusiasts by offering precise and timely insights into plant health and growth. This will allow them to make well-informed decisions about plant care and optimize growth conditions. By providing an economical, scalable, and effective system with numerous applications in horticulture, agriculture, and environmental research, this research project seeks to transform plant growth monitoring.

关键词： Temperature sensors Optical fiber sensors Computer vision Plants (biology) Feature extraction Internet of Things Biomedical monitoring Monitoring Intelligent sensors Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Advanced Medical image processing for Lung Cancer Detection using machine Learning Algorithms

Advanced Medical Image Processing for Lung Cancer Detection ...

引用

International conference on Inventive Computation Technologies (ICICT)

作者： P. Karthika T. Papitha Christobel Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram Chennai

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

One of the main causes of cancer-related deaths is lung cancer, and increasing survival rates requires early detection. The use of sophisticated machine learning (ML) algorithms to improve the identification of lung cancer from chest X-rays and CT images is investigated in this study. vision Transformers (viT), Reinforcement Learning (RL), Generative Adversarial Networks (GANs), Meta-Learning, and Ensemble Learning are some of the state-of-the-art methods we use. Compared to conventional CNNs, By capturing long-range dependencies in medical pictures, vision Transformers (viT) can improve accuracy by up to 85–96% compared to typical CNN models, especially when it comes to feature extraction and categorization. The optimization of diagnostic workflows using Reinforcement Learning (RL) results in a 90% improvement in decision-making efficiency and adaptive learning capabilities, which greatly enhance real-time picture analysis. Training datasets can be enhanced with Generative Adversarial Networks (GANs), which create realistic synthetic images and increase model generalization by 90–97%. This is crucial in situations where data is scarce or unbalanced. For uncommon or underrepresented cancer cases, meta-learning improves classification accuracy by 90% by allowing models to learn from sparsely labeled data. Ensemble learning reduces bias and variation by combining many models using approaches like XGBoost, Bagging, and Stacking, increasing total accuracy by 80–95%. When compared to conventional methods, key performance indicators including precision, recall, and F1 score demonstrate significant gains, with sensitivity rising by up to 90% and specificity improving by 85%. These cutting-edge algorithms greatly improve the detection of lung cancer, facilitating quicker and more precise diagnoses and assisting clinicians in making decisions that will benefit patients.

关键词： Metalearning Computer vision Adaptation models machine learning algorithms Accuracy Lung cancer Reinforcement learning Transformers Generative adversarial networks Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

An Innovative Decision Support Glucose Metabolism Disorder Prediction Approach Based on Norm-Learning and Pre-Trained Architecture Pattern

An Innovative Decision Support Glucose Metabolism Disorder P...

引用

International conference on Inventive Computation Technologies (ICICT)

作者： PreethaRajagopalan. M Anuratha. V Elamparithi. M Department of Computer Science Kamalam College of Arts and Science Anthiyur Bharathiar University Coimbatore Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Glucose metabolism disorder has the potential to adversely affect various organs, including the eyes, coronary arteries, and kidneys, through multiple mechanisms. Consequently, it ranks among the foremost contributors to disability and premature mortality globally. The number of deaths from glucose metabolism disorder is increasing year by year, so developing a system that can correctly detect people with glucose metabolism disorder is essential. This study presents an advanced medical decision-making system that uses L-norm Support Vector machine with pre-trained UNet (ι - SVM with UNet) to forecast glucose metabolism disorder. These state-of-the-art algorithms, well-known in computer vision, image analysis, and natural language processing, show promise in producing highly accurate outcomes when used for predictive and medical diagnostic applications. Furthermore, their integration with medical data can significantly improve the decision-making process's adaptability, transparency, and effectiveness. A comparative analysis is conducted between the ι - SVM with UNet algorithm's performance, various established methodologies, and several widely used machine learning (ML) techniques. The findings of the study show that the suggested approach, which is based on ι - SVM with UNet technology, performs admirably, attaining an F1-score of 96% and an accuracy of 96%. This enhancement can increase the correctness of the final judgment while saving effort, labor, and time in healthcare services.

关键词： Support vector machines Representation learning Accuracy Decision making Mortality Prediction algorithms Natural language processing Glucose Metabolism Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Deepfake image and video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

来源：评论

学校读者我要写书评

暂无评论

Intelligence Pest Detection and Control in Agriculture using Computer vision and Deep Learning

Intelligence Pest Detection and Control in Agriculture using...

引用

International conference on ent Computing and Control Systems (ICICCS)

作者： S. Ashok Kumar Fahimuddin Shaik B. Yashwitha P. vidyavathi A.Uma Maheswari P. Spurthi Department of AI&DS Annamacharya University Andhra Pradesh Department of ECE Annamacharya University Andhra Pradesh Department of AI&DS Annamacharya Institute of Technology and Sciences Andhra Pradesh

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

One of the most significant challenges in agriculture over the years has been the detection and also control of pests and while it continues to this day, the advent of technology in the agricultural sector is providing solutions that are incredibly promising. Pests cause significant worldwide crop loss and represent a threat to food security and farmer livelihoods. Conventional pest management approaches revolve around labour-intensive strategies, large applications of pesticides, and reactionary measures, resulting in environmental degradation and sub-optimal crop yields. This work utilizes the power of AI(Artificial Intelligence),computer vision and deep learning to create a smart pest detection system. It treats pest identification and damage detection as real-time visual data processing tasks with machine learning approaches, making it possible to swiftly and accurately locate pests and their damage in the photos taken through the camera, facilitating extra prompt preventive measures. Working under the hood, the algorithms adapt to changing environmental conditions and shifting pest behaviours, learning as they go. Most of the pest detection systems today are manual, requiring manual visual inspection and image recognition approaches, this solution not only provides the next level of system but it also paves the way for pest management in the most efficient, accurate & sustainable way.

关键词： Deep learning Computer vision visualization Accuracy Pesticides Manuals Control systems Real-time systems Agriculture Farming

来源：评论

学校读者我要写书评

暂无评论

Data Fusion Approach for Unmodified UAV Tracking with vision and mmWave Radar

Data Fusion Approach for Unmodified UAV Tracking with Vision...

引用

International conference on Unmanned Aircraft Systems (ICUAS)

作者： Guilherme Amaral João J. Martins Pedro Martins André Dias José Almeida Eduardo Silva INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto rua Dr. António Bernardino de Almeida Porto Portugal

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

The knowledge of the precise 3D position of a target in tracking applications is a fundamental requirement. The lack of a low-cost single sensor capable of providing the three-dimensional position (of a target) makes it necessary to use complementary sensors together. This research presents a Local Positioning System (LPS) for outdoor scenarios, based on a data fusion approach for unmodified UAV tracking, combining a vision sensor and mmWave radar. The proposed solution takes advantage of the radar's depth observation ability and the potential of a neural network for image processing. We have evaluated five data association approaches for radar data cluttered to get a reliable set of radar observations. The results demonstrated that the estimated target position is close to an exogenous ground truth obtained from a visual Inertial Odometry (viO) algorithm executed onboard the target UAV. Moreover, the developed system's architecture is prepared to be scalable, allowing the addition of other observation stations. It will increase the accuracy of the estimation and extend the actuation area. To the best of our knowledge, this is the first work that uses a mmWave radar combined with a camera and a machine learning algorithm to track a UAV in an outdoor scenario.

关键词： visualization Target tracking Radar clutter Airborne radar Data integration Radar imaging vision sensors Radar tracking Autonomous aerial vehicles Millimeter wave communication

来源：评论

学校读者我要写书评

暂无评论

12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

12th Ecuadorian Conference on Information and Communication ...

引用

12th Ecuadorian conference on Information and Communication Technologies, TICEC 2024

ISBN: (纸本)9783031754302

The proceedings contain 24 papers. The special focus in this conference is on Information and Communication Technologies. The topics include: Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network;Using Artificial Intelligence and X-ray images to Train and Predict COviD-19 and Pneumonia: Tool for Diagnosis and Treatment;Region of Interest Features and Classification of MRI Brain Lesions;deep Learning-Based Leukemia Diagnosis from Bone Marrow images;Breast Thermographic image Augmentation Using Generative Adversarial Networks (GANs);Evaluating Histopathological Cancer Detection: A Comparative Analysis of CNN Architectures for Tumor Detection in Lymph Node Pathology;viTSigat: Early Black Sigatoka Detection in Banana Plants Using vision Transformer;automatic Parking Space Segmentation Using K-Means Clustering and image processing Techniques;neural Agents with Continual Learning Capacities;centinela: An Intelligent System Based on an Integrated Architecture for Supporting Scholars;Optimizing Predictive Models in Healthcare Using Artificial Intelligence: A Comprehensive Approach with a COviD-19 Case Study;predictive Model Proposal in Telemetry Using machine Learning Techniques to Anticipate Water Degradation in Aquaculture;Evaluation of the Use of Artificial Intelligence Techniques in the Mitigation of the Broadcast Storm Problem in FANET Networks;adaptation Dynamics of Galápagos Finches: Evolutionary Responses to Climate Variation Explored through machine Learning;incident Alert Priority Levels Classification in Command and Control Centre Using Word Embedding Techniques;physics Informed Neural Networks and Gaussian Processes-Hamiltonian Monte Carlo to Solve Ordinary Differential Equations;Pre-processing of the Text of ECU 911 Emergency Calls;Secure BLE Communication Between Android Devices and Embedded Systems for IoT applications;modeling and Control of a Peltier Thermoelectric System Applying a Multi-objective Optimization Approach;wearable Device for Ac

关键词：

来源：评论

学校读者我要写书评

暂无评论

Proceedings of SPIE-IS and T Electronic Imaging - image processing: machine vision applications vi

Proceedings of SPIE-IS and T Electronic Imaging - Image Proc...

引用

image processing: machine vision applications vi

ISBN: (纸本)9780819494344

The proceedings contain 35 papers. The topics discussed include: a polynomial phase-shift algorithm for high precision three-dimensional profilometry;high-temperature dual-band thermal imaging by means of high-speed CMOS camera system;a state observer for using a slow camera as a sensor for fast control applications;multiple-level patch-based object tracking using MLBP-based integral histogram;periodicity estimation of nearly regular textures based on discrepancy norm;gradient feature matching for in-plane rotation invariant face sketch recognition;an iris segmentation algorithm based on edge orientation for off-angle iris recognition;dense sampling of shape interiors for improved representation;efficient defect detection with sign information of Walsh Hadamard transform;improving the performance of interest point detectors with contrast stretching functions;and object detection using feature-based template matching.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Python-Based Optical Character Recognition (OCR)

Python-Based Optical Character Recognition (OCR)

引用

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Priyanka Kaushik Priyanka Rawat Devyansh Batra P. Vensheeba Delin Saurabh Pratap Singh Rathore S. Kaliappan Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science and Engineering Chandigarh University Chandigarh India Dept. of Civil Eng. Dhanalakshmi Srinivasan College of Eng. Coimbatore India Dept. of Management & Research ICAPSR New Delhi India Division of Research and Development Lovely Professional University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Optical Character Acknowledgment (OCR) stands as a transformative innovation at the crossing point of computer vision and machine learning, encouraging the extraction of printed data from pictures or checked archives. Within the period of advanced change, OCR serves as a foundation for different applications, such as archive digitization, content mining, and cleverly data recovery. The first thing that pops into one’s mind in building vigorous and versatile OCR frameworks for different scenarios is the choice of a successful programming dialect. The Python programming dialect is discussed here, which is renowned for its simplicity, vast libraries, and dynamic community, as an impressive tool to execute OCR arrangements.

关键词： Technological innovation image processing Optical character recognition Pipelines Programming Libraries Data mining Reliability Python Testing

来源：评论

学校读者我要写书评

暂无评论

Traffic Management System Using Computer vision 5th

Traffic Management System Using Computer Vision

引用

5th International conference on Recent Trends in machine Learning, IoT, Smart Cities, and applications, ICMISC 2024

作者： Jeslin Shanthamalar, J. Yuthika, P. Kannan, R. Albert Mayan, J. Velvizhi, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819788644

It is essential to find creative solutions to the growing urban problems of traffic congestion and parking issues. By using real-time traffic camera photos with image processing and deep learning algorithms to compute traffic density in real-time, the suggested approach gets around these problems. The system speeds up transit and lowers pollution in megacities by reducing traffic dramatically and dynamically modifying traffic lights based on vehicle density. The study also presents a novel solution to the issue of people circling open lots in search of parking spots. The system achieves a remarkable positive detection rate by using thermal cameras and cutting-edge deep learning architectures using Yolov4. The overall accuracy of the YOLO v4 model in this hypothetical scenario is approximately 0.90. Parking lot cruising patterns are illuminated by the precise tracking of several moving automobiles made possible by Kalman filters. In addition to improving traffic control, this combination of state-of-the-art technologies provides insightful information about parking behavior, opening the door to more effective urban mobility solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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330页 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：