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

2,980 篇 会议
261 册 图书
58 篇 期刊文献
1 篇 学位论文

馆藏范围

3,300 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
532 篇 computer vision
243 篇 machine learning
204 篇 image segmentati...
198 篇 feature extracti...
192 篇 artificial intel...
158 篇 image processing...
150 篇 pattern recognit...
144 篇 deep learning
133 篇 cameras
91 篇 computational mo...
91 篇 training
85 篇 visualization
84 篇 object detection
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

3,215 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 3300 条记录，以下是51-60 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：

Enhancing Diverse Intra-identity Representation for visible-Infrared Person Re-Identification

Enhancing Diverse Intra-identity Representation for Visible-...

引用

IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Kim, Sejun Gwon, Soonyong Seo, Kisung Seokyeong Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

visible-Infrared person Re-Identification (vi-ReID) is a challenging task due to modality discrepancy. To reduce modality-gap, existing methods primarily focus on sample diversity, such as data augmentation or generating intermediate modality between visible and Infrared. However, these methods do not consider the increase in intra-instance variance caused by sample diversity, and they focus on dominant features, which results in a remaining modality gap for hard samples. This limitation hinders performance improvement. We propose Intra-identity Representation Diversification (IRD) based metric learning to handle the intra-instance variance. Specifically IRD method enlarge the Intra-modality Intra-identity Representation Space (IIRS) for each modality within the same identity to learn diverse feature representation abilities. This enables the formation of a shared space capable of representing common features across hetero-modality, thereby reducing the modality gap more effectively. In addition, we introduce a HueGray (HG) data augmentation method, which increases sample diversity simply and effectively. Finally, we propose the Diversity Enhancement Network (DEN) for robustly handling intra-instance variance. The proposed method demonstrates superior performance compared to the state-of-the-art methods on the SYSU-MM01 and RegDB datasets. Notably, on the challenging SYSU-MM01 dataset, our approach achieves remarkable results with a Rank-1 accuracy of 76.36% and a mean Average Precision (mAP) of 71.30%.

关键词： Algorithms Algorithms and algorithms formulations image recognition and understanding machine learning architectures

来源：评论

学校读者我要写书评

暂无评论

Swin on Axes: Extending Swin Transformers to Quadtree image Representations

Swin on Axes: Extending Swin Transformers to Quadtree Image ...

引用

IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Oliu, Marc Nasrollahi, Kamal Escalera, Sergio Moeslund, Thomas B. Aalborg Univ Fredrik Bajers Vej 7KOst Aalborg Denmark Milestone Syst Banemarksvej 50 Brondby Denmark Univ Barcelona Gran Via Corts Catalanes 585 Barcelona Spain Comp Vis Ctr Campus UABEdifici O Cerdanyola Del Valles Spain

ISBN: (纸本)9798350370287;9798350370713

In recent years, Transformer models have revolutionized machine learning. While this has resulted in impressive results in the field of Natural Language processing, Computer vision quickly stumbled upon computation and memory problems due to the high resolution and dimensionality of the input data. This is particularly true for video, where the number of tokens increases cubically relative to the frame and temporal resolutions. A first approach to solve this was vision Transformers, which introduce a partitioning of the input into embedded grid cells, lowering the effective resolution. More recently, Swin Transformers introduced a hierarchical scheme that brought the concepts of pooling and locality to transformers in exchange for much lower computational and memory costs. This work proposes a reformulation of the latter that views Swin Transformers as regular Transformers applied over a quadtree representation of the input, intrinsically providing a wider range of design choices for the attentional mechanism. Compared to similar approaches such as Swin and MaxviT, our method works on the full range of scales while using a single attentional mechanism, allowing us to simultaneously take into account both dense short range and sparse long range dependencies with low computational overhead and without introducing additional sequential operations, thus making full use of GPU parallelism.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Generation of 3D LWIR thermal maps based on deep learning SLAM: feasibility and evaluation 6

Generation of 3D LWIR thermal maps based on deep learning SL...

引用

conference on Artificial Intelligence and machine Learning for Multi-Domain Operations applications vi

作者： Kim, Donyung Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan Gyeongsangbuk D South Korea

ISBN: (纸本)9781510674219;9781510674202

machine Learning has played a major role in various applications including visual Slam and themal image process. In this paper, we discussed the possibility of generating a thermal map using LWIR images and a deep learning-based visual slam network and the value that the thermal map can create. We summarized the advantages and applicability of various deep learning-based visual slams and confirmed the results of nice slam, which generates the most curious Dense map. In order to apply visual SLAM technology, time series, scene repetition, and images from various angles for one scene are required. However, most LWIR data sets consist of one shot for each scene or are unidirectional driving data. To solve this, we created a scenario using the LWIR driving dataset and created a repetitive route through repetition. RGB-Depth SLAM Mapping was performed on the constructed data set, and the results were evaluated and the limitations of the current approach were discussed. Finally, we summarized future directions for creating stable 3D thermal maps in indoor and outdoor environments by resolving the limitations.

关键词： Multi-Modal image processing vis-Lwir SLAM Thermal Map Deep-Learning 3D mapping CNN Multispectral

来源：评论

学校读者我要写书评

暂无评论

LFRNet: Low-Light Face Super-Resolution with Light Frequency Representation 2

LFRNet: Low-Light Face Super-Resolution with Light Frequency...

引用

2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Zha, Bingxin Zhu, Huijie Zhang, Haolin Yang, Shengying School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou313000 China Co. Ltd Shanghai200233 China

ISBN: (纸本)9798350390254

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications. © 2024 IEEE.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bionic model of information processing in the retina

Bionic model of information processing in the retina

引用

2024 International conference on Artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Bohlmann, Sabine Clausthal University of Technology Institute of Computer Science Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350394528

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences. © 2024 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Realistic and visually-Pleasing 3D Generation of Indoor Scenes from a Single image 7th

Realistic and Visually-Pleasing 3D Generation of Indoor Scen...

引用

7th Chinese conference on Pattern Recognition and Computer vision

作者： Li, Jie Wang, Lei Chen, Gongbin Li, Ang Qiu, Yuhao Wu, Jiaji Cheng, Jun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci CAS Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.

关键词： 3D mesh Reconstruction Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Review on Quantum machine Learning in Different Computer vision Fields

A Review on Quantum Machine Learning in Different Computer V...

引用

2024 IEEE International Performance, Computing, and Communications conference, IPCCC 2024

作者： Islam, Md Majedul He, Jing Selena Kennesaw State University Department of Computer Science Marietta United States

ISBN: (纸本)9798350367942

Quantum machine Learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field. © 2024 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Unveiling the vision: A Comprehensive Review of Computer vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

引用

2024 International conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, Computer vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Design Of Convolution Neural Network For Crack Detection 7

Design Of Convolution Neural Network For Crack Detection

引用

7th International conference on Devices, Circuits and Systems (ICDCS)

作者： Malathi, D. Gautham, S. Dineshkumar, M. Balakrishnan, K. Kongu Engn Coll Dept Elect & Commun Engn Erode India

ISBN: (纸本)9798350350470;9798350350487

Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These Neural Networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer vision applications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.

关键词： crack detection deep learning convolution neural network image segmentation CNN system image processing image classification

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 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：