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

1,650 篇 会议
71 篇 期刊文献

馆藏范围

1,721 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,438 篇 工学
- 814 篇 电气工程
- 447 篇 计算机科学与技术...
- 175 篇 测绘科学与技术
- 163 篇 电子科学与技术（可...
- 152 篇 仪器科学与技术
- 130 篇 材料科学与工程（可...
- 94 篇 航空宇航科学与技...
- 82 篇 生物医学工程（可授...
- 78 篇 软件工程
- 67 篇 信息与通信工程
- 60 篇 控制科学与工程
- 25 篇 机械工程
- 15 篇 光学工程
- 6 篇 交通运输工程
- 5 篇 环境科学与工程（可...
- 5 篇 安全科学与工程
- 4 篇 力学（可授工学、理...
- 4 篇 土木工程
1,370 篇 理学
- 1,321 篇 物理学
- 161 篇 数学
- 29 篇 化学
- 15 篇 天文学
- 14 篇 大气科学
- 11 篇 生物学
- 5 篇 统计学（可授理学、...
- 4 篇 海洋科学
543 篇 医学
- 439 篇 临床医学
- 106 篇 特种医学
- 22 篇 基础医学(可授医学...
11 篇 管理学
- 10 篇 管理科学与工程(可...
5 篇 农学
2 篇 法学
2 篇 军事学
1 篇 教育学

主题

1,721 篇 detection and tr...
639 篇 sensors
475 篇 image processing
428 篇 algorithm develo...
327 篇 target detection
293 篇 image segmentati...
247 篇 data modeling
182 篇 error analysis
180 篇 cameras
169 篇 visualization
169 篇 feature extracti...
163 篇 image filtering
162 篇 signal processin...
161 篇 neural networks
144 篇 signal to noise ...
143 篇 video
133 篇 databases
132 篇 computer simulat...
129 篇 binary data
121 篇 radar

机构

13 篇 carnegie mellon ...
9 篇 univ chicago dep...
9 篇 lockheed martin ...
8 篇 naval surface wa...
8 篇 nichols research...
8 篇 univ connecticut...
7 篇 jet propulsion l...
6 篇 univ new orleans...
6 篇 suny/buffalo
6 篇 univ. of califor...
6 篇 us army res lab ...
6 篇 air force wright...
5 篇 harvard univ.
5 篇 environm res ins...
5 篇 colorado state u...
5 篇 naval research l...
5 篇 texas instrument...
5 篇 hughes aircraft ...
5 篇 wright state uni...
5 篇 res lab adelphi ...

作者

15 篇 mahalanobis a
15 篇 casasent d
13 篇 bar-shalom yaako...
8 篇 willett peter
8 篇 healey g
8 篇 mahler r
7 篇 ranney k
7 篇 bar-shalom y
7 篇 blair wd
6 篇 ravichandran g
6 篇 gader pd
6 篇 chen xinjia
6 篇 nasrabadi nasser...
5 篇 shen zk
5 篇 zurbrick l
5 篇 pan xiaochuan
5 篇 meola joseph
5 篇 romeo kevin
5 篇 nguyen l
5 篇 frigui hichem

语言

1,702 篇 英文
19 篇 其他

检索条件"主题词=Detection and tracking algorithms"

共 1721 条记录，以下是1591-1600 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：

Gate gap detection method based on double checkerboard calibration 4

Gate gap detection method based on double checkerboard calib...

引用

4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Yang, Quanlin Huang, Xiaoliang Ren, Dexin Zhu, Xiaolong Yangtze River Three Gorges Navigation Administration Bureau Hubei Yichang China

ISBN: (纸本)9781510682528

The stable closure of the gate affects the safety performance and normal operation of the gate, and it is necessary to detect the gap distance of the closed door, but the direct observation of the gate monitoring image by the naked eye alone cannot achieve high precision and long-term accuracy. In view of the shortcomings of the existing technology, this paper proposes a gate gap detection method based on double checkerboard calibration, which can accurately detect the distance between the two gates and improve the detection accuracy. Compared with traditional methods, the method in this paper can reduce the dependence on gate and camera installation positions, thereby reducing maintenance costs. © 2024 SPIE.

关键词： Cameras Calibration Image processing Imaging systems detection and tracking algorithms Distortion Image segmentation Zoom lenses Camera calibration Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Investigations in the identification of fingerprint patterns by machine methods

Investigations in the identification of fingerprint patterns...

引用

Computerized Imaging Techniques 1967

作者： Gaffney, John E. IBM Corporation RockvilleMD United States

For over a decade, there has been an extensive effort by government and university research groups as well as private industry toward the development of machine systems for the "recognition" of machine printed alpha-numeric characters. Other related development efforts have been devoted to systems for the recognition of cursive writing, based on measurements taken of the motion of the stylus during the time the written material is being com-posed. It is only more recently that various organizations, among them IBM, have been investigating machine methods, including those having interaction with human operators for the realization of various functions, for recognizing or identifying fingerprints. There are some common requirements between machine processing of alpha-numeric characters and fingerprint patterns for identification purposes. One point of commonality is the need for scanning the source material, and converting it to a representation suitable for machine processing. Another mutual requirement is that of sufficiently enhancing the source image so that it can be successfully processed, and the features for machine identification extracted. Still another need shared by character recognition and fingerprint identification is the matching of the features of the pattern or image to be identified and similar images available in a library or storage facility.

关键词： Image processing Software Image storage Binary data Diamond Fingerprint recognition Data processing detection and tracking algorithms Diffraction Scanners

来源：评论

学校读者我要写书评

暂无评论

Fan blade surface defect recognition algorithm based on improved ResNet50

Fan blade surface defect recognition algorithm based on impr...

引用

2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Miao, Yilin Tang, Yuhong Pan, Xiaojian Liu, Long Zhai, Yunfeng Li, Jianjun Inner Mongolia University of Science and Technology Baotou China

ISBN: (纸本)9781510682597

At present, the traditional recognition methods such as naked eye recognition and machine vision are mainly used for the surface defect image recognition of fan blades, and their robustness is poor. In order to improve the recognition accuracy of fan blade surface defects, an improved ResNet50 algorithm is proposed in this paper. 1416 fan blade images were captured by UAV, and the data set of fan blade surface defects was established through screening, classification and data expansion. In order to verify the validity of the experiment, the data sets of sand holes and oil pollution defects of fan blades were subsequently supplemented in this experiment, and the same data set processing method was used. With ResNet50 as the backbone feature extraction network, the channel attention mechanism SENet is introduced to enhance the reuse rate of useful features and suppress useless features. In order to reduce the risk of inactivation of some neurons using ReLu activation function, this paper uses Leaky-ReLU activation function to redesign the residual block. The experimental results show that the classification accuracy of the algorithm on the self-made data is up to the baseline model, which can meet the actual demand. © 2024 SPIE.

关键词： Fluctuations and noise Data modeling Defect detection Image enhancement detection and tracking algorithms Image classification Image processing Education and training Feature extraction Neurons

来源：评论

学校读者我要写书评

暂无评论

Research on point-based object detection algorithm for small objects in point clouds

Research on point-based object detection algorithm for small...

引用

2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Dong, Wei Li, Yueyang School of Artificial Intelligence and Computer Jiangnan University Wuxi214122 China

ISBN: (纸本)9781510680425

Due to the characteristics of unordered and sparse point cloud data, as well as large scale variations and diverse shapes, existing detectors have low accuracy in detecting small objects. To address the problem of difficult small object detection in point clouds, this paper proposes a Point-based Small Object detection (PB-SOD) algorithm model. By introducing a new category-aware sampling layer, the perception ability of the model is improved, thereby enhancing the detection accuracy of small objects. At the same time, a new data augmentation strategy is proposed to improve the generalization ability of the model. Experimental results on the KITTI dataset demonstrate that the PB-SOD model achieves better detection performance for the pedestrian class, a small object category, compared to the baseline model IA-SSD and other mainstream models, effectively completing small object detection tasks on point cloud datasets. © 2024 SPIE.

关键词： Object detection Point clouds Education and training detection and tracking algorithms Feature extraction Ablation

来源：评论

学校读者我要写书评

暂无评论

A comparison of the wire frame and mathematical morphology approaches to machine vision

A comparison of the wire frame and mathematical morphology a...

引用

Image Pattern Recognition: Algorithm Implementations, Techniques, and Technology 1987

作者： Fox, Richard Q. Gould Vision Systems 1200 W. Colonial Drive OrlandoFL32804 United States

The technology of machine vision systems has matured rapidly over the past four years. Over 100 small companies have brought products to market aimed at putting "intelligent eyes" on the factory floor. In the early days of the machine vision industry, many different technological approaches were tried. The first practical systems were based on binary images which were processed by a frame grab, window, and pixel count. The SRI algorithms extended the binary technology to the point that object recognition and location was a possibility. It quickly became clear that this type of technology was not robust enough for dependable operation on the factory floor. Customers demanded systems that could operate for months at a time unattended, even if the intensity of light changed or the fixturing of the objects became sloppy. In addition, potential customers were attracted to the technology of machine vision, only to find out that processing speeds were too slow.

关键词： Mathematical morphology detection and tracking algorithms Pattern recognition Machine vision Digital filtering Cameras Video Image processing Shape analysis Binary data

来源：评论

学校读者我要写书评

暂无评论

Lightweight YOLOv8 for grape leaf lesion detection 3

Lightweight YOLOv8 for grape leaf lesion detection

引用

2024 3rd International Conference on Image Processing, Object detection, and tracking, IPODT 2024

作者： Ren, Yuhao Huang, Jinyu School of Computing Science and Engineering Sichuan University of Science & Engineering Yibin644000 China

ISBN: (数字)9781510685529

ISBN: (纸本)9781510685512

In the field of smart agriculture, the rapid and accurate detection of grape leaf diseases is crucial, especially for early-stage small lesions. To enhance the efficiency of detecting grape leaf diseases in resource-limited environments, this study presents an optimized lightweight model based on the YOLOv8n framework, named YOLO-BMCA. By integrating the upgraded ADown module and incorporating CGBlock enhancements, the model not only improves the efficiency of capturing both local and global information but also significantly reduces the consumption of computational resources. The adoption of BiFPN strengthens the multi-level feature fusion, while the MLCA mechanism focuses on enhancing the capture of key features, thereby increasing accuracy. In target detection of grape leaf diseases, YOLO-BMCA achieved an accuracy rate of up to 94.7%, with improvements of 2%, 1.1%, and 3.3% in the recall rate, mAP50, and mAP50-90 respectively, compared to the original algorithm. Meanwhile, the model's parameter volume and size were also significantly reduced by 58.6% and 55%, respectively, demonstrating the strong applicability of the optimized model in resource-constrained environments. © 2024 SPIE.

关键词： Diseases and disorders Performance modeling Feature extraction Data modeling detection and tracking algorithms Convolution Feature fusion Deep learning Agriculture Target detection

来源：评论

学校读者我要写书评

暂无评论

Some automatic film reading activities in the United States, 1965 - 1970

Some automatic film reading activities in the United States,...

引用

Photo-Optical Instrumentation: Present and Future Developments 1971

作者： Kasnitz, Harold L. Lincoln Laboratory Massachusetts Institute of Technology United States

The 1960's will be remembered as the time when computers began to be applied to problems of technical, scientific and economic importance. In photo optical engineering, one illustration of this development is the application of computers to the processing of pictorial imagery. Techniques of picture filtering, band-width compression and pattern recognition have been studied, which use rather sophisticated mathematical techniques and rely for their application on large digital computers.

关键词： Computing systems Pattern recognition Ions Blood Calibration Photography Photomicroscopy Computer programming detection and tracking algorithms Absorbance

来源：评论

学校读者我要写书评

暂无评论

Research on surface micro-defect detection method for metal parts based on YOLOv5

Research on surface micro-defect detection method for metal ...

引用

2024 International Conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Xiao, Biao School of Mechanical Energy and Engineering Shaoyang University Hunan Shaoyang China

ISBN: (纸本)9781510685437

To improve the accuracy of identifying non-conforming parts and improve production efficiency, this paper proposes an intelligent identification method for micro defects in metal parts based on the YOLOv5 algorithm. This method first preprocesses the collected images and then extracts part features by enhancing fixed features, extracting image defect feature points, and training the YOLO network. Then, based on the YOLOv5 algorithm, an intelligent recognition model is established to achieve intelligent recognition of micro defects in parts. The experimental results show that the detection accuracy of this method is higher than 93.9%, with an average detection time of 2.57ms, which is better than the comparison method and has an ideal detection effect. © 2024 SPIE.

关键词： detection and tracking algorithms Metals Feature extraction Education and training Defect detection Data modeling Image processing Data acquisition Edge detection Target detection

来源：评论

学校读者我要写书评

暂无评论

Research on safety detection algorithm for power work sites based on improved YOLOX

Research on safety detection algorithm for power work sites ...

引用

2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Zhu, Bing Zhu, Bin Li, Juan Shenyang Electric Power Survey & Design Institute Co. Ltd. Shenyang China

ISBN: (纸本)9781510682597

This article proposes an improved YOLOX model algorithm to improve the efficiency and accuracy of safety detection in power work sites, addressing safety risks such as improper wearing of safety equipment and environmental factors. This is crucial for preventing production accidents. This algorithm is optimized for three major challenges: small object detection, multi-target diversification, and complex backgrounds. By introducing shallow feature layer fusion, receptive field module RFB to enhance the ability of diverse object detection, and ECA Net attention mechanism to improve target differentiation in complex backgrounds, high-precision and real-time security detection has been achieved. The experimental results show that the new algorithm achieves mAP of 92.29% and detection speed of 36.06 FPS on a specially designed dataset, which is significantly better than the basic model, verifying the effectiveness and practicality of the algorithm. © 2024 SPIE.

关键词： Object detection Safety detection and tracking algorithms Signal attenuation Data modeling Education and training Evolutionary algorithms Image processing Inspection Target detection

来源：评论

学校读者我要写书评

暂无评论

Research on synthetic aperture radar recognition method based on deep learning

Research on synthetic aperture radar recognition method base...

引用

2024 International Conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Hua, Zhendong Wang, Haifeng Wang, Haoxin Chinese Flight Test Establishmen Xi’an China

ISBN: (纸本)9781510685840

Target recognition in SAR images is a key issue in remote sensing image processing, which is widely used in many fields. Traditional recognition methods face many challenges due to the complexity of SAR images. In this paper, we propose an improved method based on GoogLeNet and improved YOLOV5 algorithm to cope with the limitations of MSTAR dataset. In this paper, GoogLeNet is introduced to improve the performance of target recognition in complex backgrounds, and its unique module improves the efficiency while reducing the parameters, which is especially suitable for handling small targets and complex textures in SAR images. Meanwhile, YOLOV5 is improved to enhance the detection accuracy and reduce the computational complexity. The effectiveness and superiority of the method is verified through experiments on the MSTAR dataset. © 2024 SPIE.

关键词： Synthetic aperture radar Target detection Convolution Target recognition Education and training detection and tracking algorithms Feature extraction Image processing Performance modeling Neck

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173页 << < 156 157 158 159 160 161 162 163 164 165 > >>

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：