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

283 篇 会议
41 册 图书
3 篇 期刊文献

馆藏范围

327 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

171 篇 工学
- 152 篇 计算机科学与技术...
- 93 篇 软件工程
- 47 篇 光学工程
- 35 篇 信息与通信工程
- 28 篇 生物工程
- 22 篇 电气工程
- 16 篇 控制科学与工程
- 9 篇 电子科学与技术（可...
- 8 篇 化学工程与技术
- 8 篇 生物医学工程（可授...
- 6 篇 网络空间安全
- 5 篇 机械工程
- 5 篇 安全科学与工程
- 3 篇 仪器科学与技术
- 3 篇 材料科学与工程（可...
- 3 篇 建筑学
- 3 篇 农业工程
78 篇 理学
- 48 篇 物理学
- 30 篇 生物学
- 25 篇 数学
- 11 篇 统计学（可授理学、...
- 8 篇 化学
21 篇 医学
- 16 篇 临床医学
- 4 篇 公共卫生与预防医...
- 3 篇 基础医学(可授医学...
- 3 篇 特种医学
18 篇 管理学
- 10 篇 管理科学与工程(可...
- 9 篇 图书情报与档案管...
- 5 篇 工商管理
4 篇 农学
- 4 篇 作物学
2 篇 法学
2 篇 教育学

主题

37 篇 computer vision
25 篇 image processing...
21 篇 artificial intel...
20 篇 pattern recognit...
20 篇 computer imaging...
17 篇 machine learning
14 篇 computer applica...
13 篇 computer systems...
12 篇 signal, image an...
11 篇 deep learning
8 篇 image processing
6 篇 computers and ed...
5 篇 vision transform...
5 篇 low-level vision
4 篇 image enhancemen...
4 篇 object detection
4 篇 cell microscopy
4 篇 image segmentati...
4 篇 graphics process...
4 篇 stereo image pro...

机构

17 篇 microsoft resear...
17 篇 microsoft res as...
12 篇 tsinghua univers...
7 篇 university of sc...
7 篇 univ sci & techn...
6 篇 national key lab...
6 篇 shanghai collabo...
6 篇 shanghai key lab...
5 篇 university of te...
5 篇 dalian universit...
5 篇 university of sy...
5 篇 institute of aut...
5 篇 shenzhen univers...
4 篇 key laboratory o...
4 篇 national enginee...
4 篇 university of ud...
4 篇 tsinghua univ pe...
4 篇 microsoft cloud ...
4 篇 university of yo...
4 篇 vision and image...

作者

8 篇 han hu
7 篇 hu han
6 篇 zuxuan wu
6 篇 min xu
5 篇 hui huang
5 篇 jing dong
5 篇 yu-gang jiang
5 篇 jiwen lu
5 篇 risheng liu
5 篇 wanli ouyang
5 篇 huchuan lu
4 篇 gian luca forest...
4 篇 qi dai
4 篇 zhiguo cao
4 篇 dongdong chen
4 篇 edwin hancock
4 篇 boxin shi
4 篇 jean-jacques rou...
4 篇 zheng zhang
4 篇 andrea fusiello

语言

322 篇 英文
5 篇 其他
3 篇 中文

检索条件"任意字段=2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition, CVIPPR 2023"

共 327 条记录，以下是131-140 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：

Detecting the Foreground Dynamic Scenes using Gaussian Mixture Model Analysis Compared with Novel Principal Component Analysis 2

Detecting the Foreground Dynamic Scenes using Gaussian Mixtu...

引用

2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Baswaraju, Swathi New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350325799

Many computer vision applications rely on object recognition in video streams, and the most common technique for doing so is called foreground subtraction. updated backdrop model with a new frame. However, precise object identification is challenging in static and dynamic foregrounds. This study proposes a novel approach. Recently, Novel Principal Component Analysis, also known as (NPCA), has been used extensively across the industry for dimension reduction in image processing, presentation, and pattern identification to this issue that is based on the Gaussian Mixture Model (GMM) technique. A new updating method has been implemented to achieve a more stable model for the dynamic zones. This study provides a comprehensive overview and evaluation of other subtraction methods. The experimental results demonstrate the superiority of the proposed method over numerous top-tier object detection methods. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

No-reference image quality assessment based on deep learning 3

No-reference image quality assessment based on deep learning

引用

3rd International conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Wang, Yifan Zhang, Fan Chang, Sheng Zhang, Xinhong School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Method of Yoga Action pattern recognition Based on computer vision Technology

A Method of Yoga Action Pattern Recognition Based on Compute...

引用

Electronics, Data processing and Informatics (ACEDPI), asia-Europe conference on

作者： Ling Ma Chao Huang Wenhua College Wuhan Hubei China

Object detection and recognition using computer vision has been a very interesting and challenging research field in the past three decades. Classification and target location based on machine learning and computer vision technology has always been a hot topic, and great achievements have been made. Firstly, the classification analysis method is used to analyze and study the yoga movement and theory based on computer vision technology, including human eye tracking and recognition, face recognition, head movement tracking and recognition, gesture recognition and posture recognition. Based on the task of yoga movement pattern recognition, a yoga movement pattern recognition method based on computer vision technology is proposed. The improved model is based on the framework and structure of the network. A certain number of candidate regions are proposed and classified through feature extraction, and then these regions are output as the detected bounding box. The posture action diagram of yoga, that is, ordinary RGB color image, is collected by RGB camera, and the bone data is extracted from RGB image by bone extraction model. The RGB image data and bone data are input into the joint model, and the joint model will output the category of Yoga action and the score of this action.

关键词：

来源：评论

学校读者我要写书评

暂无评论

OTST: A Two-Phase Framework for Joint Denoising and Remosaicing in RGBW CFA

OTST: A Two-Phase Framework for Joint Denoising and Remosaic...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Fan, Zhihao Wu, Xun Meng, Fanqing Wu, Yaqi Zhang, Feng Tetras.AI University of Shanghai for Science and Technology China Tsinghua University China Tongji University China Shanghai Artificial Intelligence Laboratory China

ISBN: (纸本)9798350302493

RGBW, a newly emerged type of Color Filter Array (CFA), possesses strong low-light photography capabilities. RGBW CFA shows significant application value when low-light sensitivity is critical, such as in security cameras and smartphones. However, the majority of commercial image signal processors (ISP) are primarily designed for Bayer CFA, research pertaining to RGBW CFA is very rare. To address above limitations, in this study, we propose a two-phase framework named OTST for the RGBW Joint Denoising and Remosaicing (RGBW-JRD) task. For the denoising stage, we propose Omni-dimensional Dynamic Convolution based Half-Shuffle Transformer (ODC-HST) which can fully utilize image's long-range dependencies to dynamically remove the noise. For the remosaicing stage, we propose a Spatial Compressive Transformer (SCT) to efficiently capture both local and global dependencies across spatial and channel dimensions. Experimental results demonstrate that our two-phase RGBW-JRD framework outperforms existing RGBW denoising and remosaicing solutions across a wide range of noise levels. In addition, the proposed approach ranks the 2nd place in MIPI 2023 RGBW Joint Remosaic and Denoise competition. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Research on Intelligent Monitoring Method of External Damage Around Transmission Line Based on Millimeter Wave Radar and Camera

Research on Intelligent Monitoring Method of External Damage...

引用

2023 International conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zhang, Sihang Yang, Zhi Zhang, Chenyu Zhang, Jun Li, Danyu Zhang, Nailong Zhao, Bin Li, Mengxuan Liu, Yi Zhao, Binbin Liu, Chang China Electric Power Research Institute Beijing100192 China JiangSu Electric Power Company Nanjing210024 China JiangSu Electric Power Company Electric Power Research Institute Nanjing211103 China

ISBN: (纸本)9781510666351

In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection. © 2023 SPIE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Enhancing Text Detection in Natural Scenes: A Hybrid Approach with MSER, Connected Components, and Norm-CLAHE 3

Enhancing Text Detection in Natural Scenes: A Hybrid Approac...

引用

3rd IEEE International conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Selvam, Prabu Nevgi, Akshaj Gunasundari, C. Sowrish, V.K. Natarajan, B. Jessika, S. Sharon School of Computer Science and Engineering VIT University Chennai India School of Computer Science and Engineering VIT Chennai Vellore India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India VIT University Department of Computer Science and Engineering Vellore India School of Computer Science and Engineering VIT Vellore Vellore India

ISBN: (纸本)9798350317022

The textual information present in natural scene images is a valuable clue for many vision-based applications. Locating text areas becomes particularly challenging due to issues like varying lighting conditions, complex backgrounds, non-uniform text sizes, and more. Detecting curved text, in particular, is a demanding task in the fields of computer vision and pattern recognition. This research work proposes a robust approach for the detection of text in natural scene images with different orientations. The proposed method is divided into three key stages: (i) Pre-processing: The original image is converted to grayscale, and this research propose a new Norm-Contrast-limited Adaptive Histogram Equalization (CLAHE) to enhance the contrast in the grayscale image by limiting noise amplification and prevents saturation in dark and bright text regions. (ii) Text Region Extraction: this research combined Otsu's thresholding, Maximally Stable Extremal Region (MSER) and Sobel edge de-tector to extract text regions and enhance edges in the image. (iii) Non-Text Region Removal: In this stage, this research combine Connected Component (CC), Morphological operations, MSER region filters, and basic text geometric properties to create a framework for removing non-text regions and drawing rectangle bounding boxes around the detected text regions. this research assessed the performance of the proposed method utilizing the ICDAR 2017 text dataset, which yielded an overall accuracy of 93.47%, demonstrating highly satisfactory results. © 2023 IEEE.

关键词： connected component edge detector image processing MSER Text detection

来源：评论

学校读者我要写书评

暂无评论

Biometric Muzzle pattern recognition for Cattle Identification using Deep Learning 6

Biometric Muzzle Pattern Recognition for Cattle Identificati...

引用

6th International conference on Inventive Computation Technologies, ICICT 2023

作者： Jayakumar, Nithya Nithish Kumar, R. Ruthrakumar, A. Saikavya, K. Department of Information Technology K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798350398496

The ability to identify specific cows is a critical aspect of keeping cattle. Registration is essential for the production, distribution, and breeding of cows. It is dangerous to brand cows in the ear in the traditional manner. To solve this issue, we recommend using a biometric scanner for identification. Numerous studies have shown that the muzzle pattern can be used to distinguish animals and eliminate inconsistent patterns. This strategy might be effective in reducing spurious matching muzzle patterns. On the tip of their nose or muzzle, the majority of animals have a distinctive pattern. An animal's characteristic pattern is obvious to the unaided eye once it is born. Animals can be recognized by human muzzle patterns, just like with fingerprints. Systems for identifying animals are needed, as they would be useful for submitting loan and insurance applications. Based on a biometric muzzle scanner, the approach of machine learning, image processing, and computer vision has been assessed for identification purposes. © 2023 IEEE.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Size and Defect Detection of Valve Based on computer vision

Size and Defect Detection of Valve Based on Computer Vision

引用

2022 International conference on computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2022

作者： Yan, Xiaobo Feng, Le Tan, Mian Yang, Yuan Zhang, Yu Wang, Lin Guizhou Key Laboratory of Pattern Recognition and Intelligent System China Guizhou Minzu University Guiyang550025 China Guiyang Fusheng Intelligent Technology Co. Ltd China

ISBN: (纸本)9781510663350

Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time. © 2023 SPIE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Object Detection with YOLO Model on NAO Humanoid Robot 10th

Object Detection with YOLO Model on NAO Humanoid Robot

引用

10th Biennial International conference on pattern recognition and Machine Intelligence (PReMI)

作者： Biswas, Sougatamoy Nandy, Anup Naskar, Asim Kumar Natl Inst Technol Rourkela Rourkela Odisha India

ISBN: (纸本)9783031451690;9783031451706

Object detection is an important part in the field of robotics as it enables robots to understand their surroundings. The NAO humanoid robot is extensively used in human-robot interaction research. In this study a novel approach combining the VGG16 network and You Only Look Once (YOLO) algorithm is used for object detection using the NAO robot. YOLOv7 is selected for its best balanced information retention, quick inference, accurate localization, and identification of objects as compared to several bounding box algorithms. VGG16 network is adopted as a feature extractor to optimize the performance of object detection for NAO low-resolution camera images. Once feature extraction is completed then it's output layer is combined with our fine tuned YOLOv7 model for object detection. The fine-tuned YOLOv7 model is proposed with some pre-processing techniques such as image augmentation, angle movement, and scale resizing for the performance improvement. The efficiency of the proposed model is compared with the performance of other state-of-the-art models.

关键词： Object detection computer vision Humanoid robots Object recognition

来源：评论

学校读者我要写书评

暂无评论

LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 IEEE/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 IEEE.

关键词： Stereo image processing

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33页 << < 10 11 12 13 14 15 16 17 18 19 > >>

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：