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

8,212 篇 会议
158 篇 期刊文献
36 册 图书

馆藏范围

8,405 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

5,774 篇 工学
- 5,035 篇 计算机科学与技术...
- 3,598 篇 软件工程
- 1,534 篇 光学工程
- 865 篇 信息与通信工程
- 662 篇 电气工程
- 641 篇 控制科学与工程
- 501 篇 机械工程
- 449 篇 生物医学工程（可授...
- 372 篇 电子科学与技术（可...
- 349 篇 生物工程
- 237 篇 仪器科学与技术
- 119 篇 化学工程与技术
- 101 篇 建筑学
- 92 篇 土木工程
- 71 篇 安全科学与工程
- 57 篇 材料科学与工程（可...
- 51 篇 交通运输工程
3,196 篇 理学
- 1,979 篇 物理学
- 1,901 篇 数学
- 578 篇 统计学（可授理学、...
- 408 篇 生物学
- 126 篇 化学
- 57 篇 系统科学
486 篇 管理学
- 327 篇 图书情报与档案管...
- 175 篇 管理科学与工程(可...
- 55 篇 工商管理
424 篇 医学
- 407 篇 临床医学
- 105 篇 基础医学(可授医学...
- 79 篇 药学(可授医学、理...
54 篇 艺术学
- 53 篇 设计学（可授艺术学...
53 篇 农学
45 篇 法学
27 篇 教育学
18 篇 经济学
11 篇 军事学
5 篇 文学

主题

1,328 篇 image processing
1,100 篇 computer vision
894 篇 image segmentati...
663 篇 pattern recognit...
538 篇 image reconstruc...
515 篇 image analysis
501 篇 cameras
452 篇 layout
375 篇 shape
366 篇 computer science
317 篇 feature extracti...
268 篇 face recognition
264 篇 image recognitio...
260 篇 robustness
243 篇 humans
202 篇 pixel
200 篇 image edge detec...
192 篇 object recogniti...
189 篇 object detection
188 篇 pattern recognit...

机构

23 篇 department of co...
20 篇 microsoft resear...
17 篇 center for autom...
16 篇 the robotics ins...
15 篇 national laborat...
15 篇 institute of ima...
15 篇 institute of ima...
15 篇 department of co...
15 篇 institute of com...
14 篇 department of co...
14 篇 tsinghua univers...
14 篇 school of comput...
14 篇 nec research ins...
14 篇 school of comput...
13 篇 robotics institu...
13 篇 institute for ro...
12 篇 computer science...
11 篇 carnegie mellon ...
11 篇 swiss fed inst t...
11 篇 inria sophia-ant...

作者

31 篇 anon
27 篇 huang thomas s.
25 篇 jain anil k.
24 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 haralick robert ...
19 篇 timofte radu
18 篇 shum heung-yeung
18 篇 aggarwal j.k.
17 篇 zhang lei
17 篇 hancock edwin r.
16 篇 van gool luc
15 篇 g. healey
14 篇 davis larry s.
14 篇 rosenfeld azriel
14 篇 t. kanade
14 篇 r. szeliski
14 篇 ahuja narendra
13 篇 k. ikeuchi
13 篇 chellappa rama

语言

8,192 篇 英文
126 篇 其他
87 篇 中文
3 篇 土耳其文
1 篇 西班牙文
1 篇 葡萄牙文

检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是451-460 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：

Dynamic Digital Signage System: A Cost-Effective and Unified Web-Based Solution for Content and Analytics Management 2

Dynamic Digital Signage System: A Cost-Effective and Unified...

引用

2nd International conference on image processing.and Media Computing, ICIPMC 2023

作者： Cabading, Kriselyn Malaca, Orlando Juanatas, Ronaldo Tena, Policarpio Goh, Joselito Eduard Goh, Marie Luvett Juanatas, Irish Juanatas, Roben Technological University of the Philippines College of Engineering Manila Philippines Technological University of the Philippines College of Industrial Education Manila Philippines Technological University of the Philippines Graduate Program and External Studies Manila Philippines College of Information System de la Salle - College of Saint Benilde Manila Philippines Far Eastern University - Institute of Technology College of Computer Studies and Multimedia Arts IT Department Manila Philippines Far Eastern University Information Technology Department Quezon City Philippines National University Philippines College of Computing and Information Technology Manila Philippines

ISBN: (纸本)9798350326611

While several content management systems (CMS) and audience analytics tools are available for digital signage in the market, they are often sold separately and can be expensive. Therefore, this project aims to design a cost-effective and unified web-based solution for digital signage that combines content management and audience analytics functions, reducing the need for multiple purchases. This can be achieved by utilizing Raspberry Pi technology, known for its cost-effectiveness and versatility in integrations, along with a face recognition camera and machine learning methods. This proof of concept demonstrates the integration of these components to create a dynamic digital signage system. Overall, this project has the potential to offer an affordable solution for companies aiming to efficiently manage and optimize their digital marketing strategies, especially in the Digital Out-of-Home (DOOH) Advertising space. © 2023 ieee.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

image Enhancement Algorithm based on Local Contrast for Convolutional Neural Network-Based Infrared Target recognition: ∗Note: Subtitles are not captured in Xplore and should not be used 24

Image Enhancement Algorithm based on Local Contrast for Conv...

引用

24th ieee International conference on High Performance Computing and Communications, 8th ieee International conference on Data Science and Systems, 20th ieee International conference on Smart City and 8th ieee International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nian, BingKun Ma, LiYuan Zhang, Yan Zhang, Yi Shi, HuaJun The 32nd Institute China Electricity Science and Technology China College of Science National University of Defense Technology China College of Electronic Science National University of Defense Technology China

ISBN: (纸本)9798350319934

In the task of infrared weak and small target recognition, in order to improve the image quality and solve the problem of poor learning ability of convolutional neural network (CNN) due to the imbalance of positive and negative samples, this study proposes a three-stage image enhancement algorithm named adaptive filter for quality enhancement based on local contrast (AFQELC). Firstly, AFQELC analyzes the statistical characteristics of the image and constructs a local contrast adaptive filter (LCAF) to enhance the detailed information of the image, which promotes the deep learning model to learn low-level semantic information. Secondly, principal component analysis (PCA) fusion combines information from the original image and the image enhanced by LCAF to reduce noise. Finally, the gradient component of the original image is extracted to further revise the processing.result, which promotes the deep learning model to learn advanced semantic information. The proposed non-data-driven algorithm has a clear and interpretable process, which is superior to other traditional and neural network based image enhancement algorithms. The experiment results show that the quality of images enhanced by AFQELC is improved significantly. In addition, AFQELC can improve the recognition and positioning accuracy of the CNN-based algorithm for infrared target recognition by alleviating the imbalance of positive and negative samples. © 2022 ieee.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

CSANet: High Speed Channel Spatial Attention Network for Mobile ISP

CSANet: High Speed Channel Spatial Attention Network for Mob...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Hsyu, Ming-Chun Liu, Chih-Wei Chen, Chao-Hung Chen, Chao-Wei Tsai, Wen-Chia Ind Technol Res Inst Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781665448994

The image Signal Processor (ISP) is a customized device to restore RGB images from the pixel signals of CMOS image sensor. In order to realize this function, a series of processing.units are leveraged to tackle different artifacts, such as color shifts, signal noise, moire effects, and so on, that are introduced from the photo-capturing devices. However, tuning each processing.unit is highly complicated and requires a lot of experience and effort from image experts. In this paper, a novel network architecture, CSANet, with emphases on inference speed and high PSNR is proposed for end-to-end learned ISP task. The proposed CSANet applies a double attention module employing both channel and spatial attentions. Particularly, its spatial attention is simplified to a light-weighted dilated depth-wise convolution and still performs as well as others. As proof of performance, CSANet won 2nd place in the Mobile AI 2021 Learned Smartphone ISP Challenge with 1st place PSNR score.

关键词： Performance evaluation Runtime Pipelines Network architecture Service-oriented architecture pattern recognition image restoration

来源：评论

学校读者我要写书评

暂无评论

Logarithmic Lenses: Exploring Log RGB Data for image Classification

Logarithmic Lenses: Exploring Log RGB Data for Image Classif...

引用

conference on computer Vision and pattern recognition (CVPR)

作者： Bruce A. Maxwell Sumegha Singhania Avnish Patel Rahul Kumar Heather Fryling Sihan Li Haonan Sun Ping He Zewen Li Northeastern University Boston USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The design of deep network architectures and training methods in computer vision has been well-explored. However, in almost all cases the images have been used as provided, with little exploration of pre-processing.steps beyond normalization and data augmentation. Virtually all images posted on the web or captured by devices are processed for viewing by humans. Is the pipeline used for humans also best for use by computers and deep networks? The human visual system uses logarithmic sensors; differences and sums correspond to ratios and products. Features in log space will be invariant to intensity changes and robust to color balance changes. Log RGB space also reveals structure that is corrupted by typical pre-processing. We explore using linear and log RGB data for training standard backbone architectures on an image classification task using data derived directly from RAW images to guarantee its integrity. We found that networks trained on log RGB data exhibit improved performance on an unmodified test set and invariance to intensity and color balance modifications without additional training or data augmentation. Furthermore, we found that the gains from using high quality log data could also be partially or fully realized from data in 8-bit sRGB-JPG format by inverting the sRGB transform and taking the log. These results imply existing databases may benefit from this type of pre-processing. While working with log data, we found it was critical to retain the integrity of the log relationships and that networks using log data train best with meta-parameters different than those used for sRGB or linear data. Finally, we introduce a new 10-category 10k RAW image data set (RAW10) for image classification and other purposes to enable further the exploration of log RGB as an input format for deep networks in computer vision.

关键词： Training computer vision image color analysis Pipelines Transforms Visual systems Sensor systems

来源：评论

学校读者我要写书评

暂无评论

A dual-pathways fusion network for seeing background objects in light field

A dual-pathways fusion network for seeing background objects...

引用

2022 International conference on image, Signal processing. and pattern recognition, ISPP 2022

作者： Song, Chengze Li, Wen Pi, Xinyu Xiong, Chao Guo, Xiaochuan The college of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9781510654846

Background objects obscured in some sub-apertures of light-field cameras can be seen by other sub-apertures. Consequently, occluded surfaces are possible to be reconstructed from LF images. So far, Current foreground occlusion elimination approaches based on LF usually extract only the complementary information about background objects among different sub-aperture images to get an occlusion-free center view, which cannot get ideal performances in reconstructing visually realistic and semantically plausible pixels for occluded areas. In this paper, we suggest a easy but efficient LF foreground occlusions elimination way using a dual-pathways fusion network, which is a encoder-decoder network using convolution operations. In our method, we first construct all sub-aperture images(SAIs) as an input tensor and then render it to the encoder to incorporate information between SAIs. In particular, except for a pathway to synthesize center view, we also set another pathway to predict the foreground occlusion. By fusing these two pathways’ outputs, we not only reserve more information belonging to occluded surfaces but also fill the occluded regions with better visual effects. Experimental results indicate that our method is superior to the state-of-the-art approaches and the occlusion-free view looks more realistic. Our source codes will be available. © 2022 SPIE.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

PiCIE: Unsupervised Semantic Segmentation using Invariance a...

引用

ieee/CVF conference on computer Vision and pattern recognition (CVPR)

作者： Cho, Jang Hyun Mall, Utkarsh Bala, Kavita Hariharan, Bharath Univ Texas Austin Austin TX 78712 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665445092

We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO [31] and Cityscapes [8] with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training.

关键词： Training Visualization image segmentation computer vision Codes Clustering methods Semantics

来源：评论

学校读者我要写书评

暂无评论

Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

引用

2023 ieee/CVF conference on computer Vision and pattern recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 ieee.

关键词： Multilayer neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Online Multi-Camera Tracking with Memory-Efficient Accumulated Appearance Features and Trajectory Validation

Efficient Online Multi-Camera Tracking with Memory-Efficient...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Lap Quoc Tran Huan Duc Vi Asilla

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and non-overlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.

关键词： computer vision Accuracy Urban areas Cameras Real-time systems Trajectory Spatiotemporal phenomena

来源：评论

学校读者我要写书评

暂无评论

Sign Language Translation and Voice Impairment Support System using Deep Learning 5

Sign Language Translation and Voice Impairment Support Syste...

引用

5th International conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shajeena, J. Shiny, R.M. Mary Vespa, M. Kavitha, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

The Sign Language Translation and Voice Impairment Support System (SLT-VISS) represents a groundbreaking application of deep learning methodology aimed at facilitating communication for individuals with hearing impairments or speech disabilities. By utilizing Convolutional Neural Network (CNN), OpenCV for image processing. and Pyttsx3 for text-to-voice conversion, SLT-VISS offers real-time translation of sign language gestures into both text and speech output. Using a camera as input, the CNN algorithm accurately identifies and classifies sign language gestures displayed in front of it. OpenCV complements this process by preprocessing.the input images for gesture segmentation and feature extraction, enhancing the accuracy of gesture recognition. Once a gesture is recognized, Pyttsx3 converts the translated text into synthesized speech, providing immediate auditory feedback to the user. SLT-VISS is designed to bridge the communication gap faced by individuals with communication disabilities, enabling them to express themselves effectively in various social and professional settings. By seamlessly translating sign language into both text and speech, this system promotes inclusivity and accessibility, empowering users to communicate with confidence and independence. The integration of CNN, OpenCV, and Pyttsx3 technologies within SLT-VISS signifies a significant advancement in assistive technology. As technology continues to evolve, further enhancements to SLT-VISS holds the potential to revolutionize communication accessibility and enable greater inclusivity in society. © 2024 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Forward-Forward Algorithm for Hyperspectral image Classification

Forward-Forward Algorithm for Hyperspectral Image Classifica...

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Abel A. Reyes-Angulo Sidike Paheding Michigan Technological University Houghton MI Fairfield University Fairfield CT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep-learning models. Its widespread adoption in fields like natural language processing. computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises.

关键词： Backpropagation Training computer vision Sensitivity Feature extraction Classification algorithms pattern recognition

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 << < 42 43 44 45 46 47 48 49 50 51 > >>

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：