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

127 篇 会议

馆藏范围

127 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

70 篇 工学
- 45 篇 计算机科学与技术...
- 26 篇 信息与通信工程
- 26 篇 软件工程
- 17 篇 光学工程
- 10 篇 电气工程
- 9 篇 电子科学与技术（可...
- 7 篇 材料科学与工程（可...
- 7 篇 生物医学工程（可授...
- 6 篇 控制科学与工程
- 5 篇 机械工程
- 5 篇 化学工程与技术
- 4 篇 仪器科学与技术
- 4 篇 交通运输工程
- 4 篇 安全科学与工程
- 4 篇 网络空间安全
- 3 篇 土木工程
- 3 篇 石油与天然气工程
- 3 篇 航空宇航科学与技...
- 3 篇 生物工程
- 2 篇 动力工程及工程热...
35 篇 理学
- 20 篇 数学
- 14 篇 物理学
- 5 篇 化学
- 5 篇 生物学
- 3 篇 统计学（可授理学、...
13 篇 管理学
- 12 篇 管理科学与工程(可...
- 3 篇 图书情报与档案管...
7 篇 医学
- 6 篇 临床医学
- 3 篇 基础医学(可授医学...
- 3 篇 公共卫生与预防医...

主题

19 篇 accuracy
18 篇 convolutional ne...
13 篇 training
12 篇 deep learning
11 篇 image processing
11 篇 signal processin...
9 篇 image segmentati...
9 篇 signal processin...
8 篇 image reconstruc...
7 篇 image enhancemen...
7 篇 real-time system...
7 篇 psnr
7 篇 feature extracti...
7 篇 image recognitio...
7 篇 image quality
6 篇 image color anal...
5 篇 support vector m...
5 篇 visualization
5 篇 machine learning...
5 篇 measurement

机构

2 篇 j.c. bose univer...
2 篇 shenyang aerospa...
2 篇 advanced semicon...
2 篇 school of comput...
2 篇 dr. d. y. patil ...
2 篇 school of comput...
2 篇 beijing - dublin...
2 篇 college of trans...
2 篇 school of electr...
2 篇 cyrinn science &...
2 篇 college of mecha...
2 篇 school of comput...
2 篇 college of compu...
2 篇 k. r. mangalam u...
2 篇 school of electr...
1 篇 department of co...
1 篇 electrical and c...
1 篇 international sc...
1 篇 gitam univ dept ...
1 篇 king mongkuts un...

作者

2 篇 yan jun
2 篇 wang changxiang
2 篇 anuradha
2 篇 ma jinshan
1 篇 teodora bakardji...
1 篇 jiang zhihao
1 篇 zhang tao
1 篇 jiayin min
1 篇 k sevitha
1 篇 kapatos c.
1 篇 bhal arpita
1 篇 c surya
1 篇 chit siang soh
1 篇 valaparla david
1 篇 mitali monalisha
1 篇 ishwari kakade
1 篇 ran an
1 篇 jia zhenhong
1 篇 gole nilesh
1 篇 mansour hajji

语言

120 篇 英文
7 篇 其他

检索条件"任意字段=4th International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2024"

共 127 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：

Adaptive Contrast Based Real-Time image and Video Dehazing 5

Adaptive Contrast Based Real-Time Image and Video Dehazing

引用

5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Joshi, Rohin Dange, Dhruv Kavya, K.S. Maiya, Sharath R Ramaiah Institute of Technology Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331528539

Dehazing algorithms have been developed in re-sponse to the need for effectively and instantaneously removing atmospheric turbidities such as mist, haze, and fog from media. the removal of haze from an image or video enables the extraction of additional details from the scene. this paper presents the development of a real-time, memory-optimized dehazing system that utilizes digital image processing techniques and NVIDIA's CUDA architecture for efficient parallel computing. the methodology incorporates a quad-tree search algorithm for efficient atmospheric light estimation, which significantly enhances dehazing accuracy. advanced contrast enhancement techniques are employed for transmission estimation to ensure clarity and visibility in dehazed images, addressing challenges such as non-uniform illumination and varying haze densities. For image restoration, the system dynamically clips values to improve brightness and minimize information loss. the transmission map's artifacts are then smoothed using a Gaussian filter, resulting in more reliable dehazing. Extensive testing on a variety of publicly accessible datasets demonstrated that the proposed model achieved comparable accuracy to numerous existing techniques, while also restoring high-quality dehazed images and videos. the system achieves a running time of 35ms per image and up to 10ms per frame for video sequences. Performance was objectively assessed using the Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), yielding a PSNR of 28.75 and an SSIM of 0.80. © 2024 IEEE.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Weak Change Detection of Wide field Foggy Video image 24

Weak Change Detection of Wide field Foggy Video Image

引用

4th international conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2024

作者： Li, Wen Jia, Zhenhong Song, Sensen Huang, Xiaohui Wang, Jiajia Zhou, Gang Shi, Fei School of Computer Science and Technology Xinjiang University Urumqi Xinjiang China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang China

ISBN: (纸本)9798400710247

Hawk-eye devices are often used in surveillance and security, and the video images collected by them have a wide field of view. Under foggy conditions, the video images captured by eagle-eye surveillance have low contrast and details are easily lost. In order to solve these problems, this paper proposes a change detection algorithm for foggy wide field video images based on the Discrete Wavelet Transform (DWT) image fusion and FCM clustering algorithm. Firstly, the improved neighborhood ratio operator and the most value mean pixel ratio operator are used to generate the disparity map, and then the DWT is used to fuse the disparity map, and after obtaining the fused disparity map, the soft threshold function and the FCM clustering algorithm are used to carry out the coarse classification and fine classification respectively, and obtain the final change detection result map. © 2024 Copyright held by the owner/author(s).

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Low Light image Classification Using MADPIP Approach 4th

Enhancing Low Light Image Classification Using MADPIP Approa...

引用

4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Pailus, Rayner Henry Alfred, Rayner Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

Many researchers are interested in finding solutions for face recognition against low light illumination, many succeeded using Histogram Equalization, Retinex theory, Illumination map estimation model, Frequency domain and Deep learning methods. Nevertheless, not all these mentioned methods can improve all types of face detection and face recognition challenges. Many researchers of face recognition agree and believe that a better-quality input image used for face detection, feature extraction, feature selection, training and testing will produce better detection and recognition. thus, this paper will evaluate other established and published algorithms and our proposed method called Multiple Adaptive Derivative Passive image processing (MADPIP). Our solution does not use only one method but multiple and does not have a single output but multiple outputs. When an image goes through multiple adaptive changes multiple times, it is no longer appropriate to call it an output but rather a multiple-processed output or a multiple adaptive derivative. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification enhancement Convolutional neural network image restoration Low light images

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

4th International conference on Innovative Manufacturing, Me...

引用

4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

ISBN: (纸本)9789819988181

the proceedings contain 50 papers. the special focus in this conference is on Innovative Manufacturing, Mechatronics and Materials Forum. the topics include: Effect of Crack Length, Depth, and Location on Natural Frequencies of Railway Track;imputation Analysis of Time-Series Data Using a Random Forest Algorithm;harnessing Machine Learning, Blockchain, and Digital Twin Technology for advanced Robotics in Manufacturing: Challenges and Future Directions;humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People;intelligent Machining Systems for Robotic End-Effectors: State-of-the-Art and Toward Future Directions;artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles;the Role and Impact of Robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review;nanomaterial in Robotics: Bridging the Gap Between Current Applications and Future Possibilities;Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis;detection of Fault Features in Remanufacturing of Automotive Components Using image processing and Computer Vision Techniques;the Significance of the thoracic Spinal Multiple Segments During Different Pick-Object Approaches;Vibration Suppression of the Flexible Beam Structure Using PID Controller Tuned by advanced Firefly Algorithm;Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE;investigation of Wheel Robot Grouser Width Parameter Effect on Robot Mobility in Soft Sand Terrain Using Sand Test Bed;automated Harmonic signal Removal-Based image Feature Extraction Technique: A Comparative Study Using Online Databases;Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) signals.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction 4th

An Improved Grey Wolf Optimization Algorithm for Heart Disea...

引用

4th international conference on advanced Intelligent Technologies, ICAIT 2023

作者： Niu, Sihan Zhou, Yifan Li, Zhikai Huang, Shuyao Zhou, Yujun Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States School of Computer Engineering and Science Shanghai University Shanghai China School of Information Sciences University of Illinois Urbana-Champaign ChampaignIL United States

ISBN: (纸本)9789819732098

this paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show potential in medical imaging but suffer from issues like overfitting and lack of interpretability due to imbalanced and scarce data. Traditional grey wolf optimization (GWO) also has its drawbacks, such as a lack of population diversity and premature convergence. this paper addresses these problems by introducing an adaptive algorithm, enhancing the standard GWO with a sigmoid function. this algorithm was extensively compared to four leading algorithms using six well-known test functions, outperforming them effectively. Moreover, by utilizing the ACGWO, we increase the robustness and generalization of the neural network, resulting in more interpretable predictions. Applied to the publicly accessible Cleveland Heart Disease dataset, our technique surpasses ten other methods, achieving 86.8% accuracy, indicating its potential for efficient heart disease prediction in the clinical setting. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Medical image processing

来源：评论

学校读者我要写书评

暂无评论

Low-Power Design of advanced image processing algorithms under FPGA in Real-Time Applications

Low-Power Design of Advanced Image Processing Algorithms und...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Yuyao Wang Department of Electrical Engineering Lamar University Beaumont Texas USA

Due to the real-time measurement required for image processing in practice, image processing technology is increasingly supplemented by hardware, such as using DSP (Digital signal processing) chips (image processing specific chips). However, when storing and processing large amounts of data, using DSP for image processing also has problems such as slow processing speed and poor real-time performance of the system. this article combines the embedded cache module in the IP core of FPGA (Field Programmable Gate Array) with the read/write ping-pong structure to store and retrieve image data, in order to save memory space on the chip. the relatively simple algorithm functions and parallel computing of FPGA, combined with a pipeline structure, shorten the image preprocessing time and solve the problem of poor real-time image processing. the experimental results show that due to the increase in workload, the static power consumption of FPGA algorithm design only increased from lW to 1.242W, while the static power consumption of traditional design increased from 1.009W to 1.578W; the dynamic power consumption of FPGA algorithm design has increased from 1.005W to 1.489W.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Analysis of the Efficiency of Quality Control algorithms for Modified Surfaces for High-Error Processes Based on 2D Miniatures and Non-visual Information 4th

Analysis of the Efficiency of Quality Control Algorithms for...

引用

4th international conference on Reliable Systems Engineering, ICoRSE 2024

作者： Bondar, Dmytro Basova, Yevheniia Vodka, Oleksii National Technical University "Kharkiv Polytechnic Institute" 2 Kyrpychova Street Kharkiv61002 Ukraine

ISBN: (纸本)9783031706691

this study investigates the efficiency of 2D thumbnail-based algorithms, complemented by non-visual information, in enhancing quality control for manufacturing processes characterized by high error rates like rimming. Amidst the challenges faced by traditional 3D technology-based quality control methods, such as extended computing times and the necessity for advanced hardware, this research proposes a different approach leveraging high-resolution 2D images and part metadata to identify defects. By employing Machine Learning techniques, specifically Mask R-CNN models enhanced with Squeeze-and-Excitation Blocks (SEBlocks) for detailed feature detection, and Random Forest classifiers for scale classification and tolerance validation, the study presents a cost-effective yet efficient alternative. the methodology encompasses four primary steps: image acquisition, enhanced hole detection, scale classification, and prediction optimization, concluding with diameter tolerance classification and validation. Employing a dataset of synthetic images annotated for hole locations, the system's performance is evaluated using metrics such as Intersection over Union (IoU), Dice score, and Mean Squared Error (MSE), demonstrating its efficacy in near-real-time defect detection. this research contributes to the field by offering an adaptable, scalable solution for quality control in manufacturing, significantly reducing dependency on costly hardware and paving the way for future innovations in process monitoring and defect detection after laser and machining processing. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： image annotation

来源：评论

学校读者我要写书评

暂无评论

Implementation and optimization of human tracking system using ARM embedded platform

Implementation and optimization of human tracking system usi...

引用

international conference on Intelligent and advanced Systems, ICIAS

作者： Shen Khang Teoh Vooi Voon Yap Chit Siang Soh Patrick Sebastian Department of Computer and Communication Technology Centre for Information Systems and Software Technologies (CISST) Universiti Tunku Abdul Rahman Perak Malaysia Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Perak Malaysia Department of Electrical and Electronic Engineering Universiti Teknologi Petronas Bandar Seri Iskandar Perak Malaysia

Computer vision is a field that includes methods for acquiring, processing, analyzing and understanding images. In the embedded world, computer vision applications have to fight with limited processing power and limited resources to achieve optimized algorithms and high performance. this paper presents work on implementing a human tracking system on both Intel based PC platform and embedded systems to optimize the algorithms for high performance. the algorithms are benchmarked on the Intel platform processor and BeagleBoard xM baed on low-power Texas Instruments (TI) DM3730 ARM processor. Functions and library in OpenCV which developed by Intel Corporation was utilized for building the human tracking algorithms.

关键词： Humans Histograms image color analysis Libraries Computer vision Cameras signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

Leveraging advanced Deep Learning Techniques for Automated Dermatological image Classification

Leveraging Advanced Deep Learning Techniques for Automated D...

引用

international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Akashdip Saha Rishik Suddapalli Abhaya Kumar Sahoo Jagannath Singh School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

this paper examines the automatic classification of skin photos using the state-of-the-art deep learning methods, with a focus on the categorization of skin lesions. One of the main goals is to improve the accuracy and user-friendliness of dermatology diagnostics; thus, the diagnosis can be made much earlier and more accurately than now, leading to the detection of skin conditions. We use convolutional neural network-based (CNN) algorithms that are highly complex and the huge international Skin Imaging Collaboration (ISIC) library for facial recognition. this model will revolutionize skin diseases diagnosis, enabling clinical decisions to be made quickly and accurately, thereby enhancing patient care and outcomes.

关键词： Deep learning Accuracy Face recognition Imaging Skin Libraries Convolutional neural networks Medical diagnosis Lesions image classification

来源：评论

学校读者我要写书评

暂无评论

Research and Analysis on image Reconstruction Algorithm Based on Compressed Sensing

Research and Analysis on Image Reconstruction Algorithm Base...

引用

Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Wenhuan Gao Lifeng Ren School of Electrical and Information Engineering Jilin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Compressed Sensing (CS) is an efficient signal processing technique that can accurately acquire and reconstruct signals at lower sampling rates exploiting the sparsity of the signals. In the field of image processing, compressed sensing image reconstruction algorithms utilize the sparse representation of images to recover the original image from small number of observations. the core of this algorithm lies in the sparse representation of the signal, the construction of the observation matrix, and the selection of the reconstruction. First, it is assumed that the original image can be sparsely represented in a certain sparse basis, such as wavelet transform or sparse dictionary. then the sparse coefficients are randomly sampled to obtain the observations. Finally, the image is reconstructed using a compressed sensing algorithm based on the sampling data and the observation matrix. performance of the reconstruction algorithm is affected by factors such as the sparsity of the signal, the design of the observation matrix, and the selection of the reconstruction algorithm To improve the reconstruction performance, various improved algorithms have been proposed in recent years, such as the iterative hard thresholding algorithm with variable step size and the deep learning image compressed sensing algorithm.

关键词： Deep learning Wavelet transforms Machine learning algorithms Sparse approximation signal processing algorithms Reconstruction algorithms Sparse matrices image reconstruction Compressed sensing Optimization

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13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：