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

1,620 篇 会议
14 册 图书
12 篇 期刊文献

馆藏范围

1,646 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,160 篇 工学
- 888 篇 计算机科学与技术...
- 587 篇 软件工程
- 313 篇 光学工程
- 270 篇 信息与通信工程
- 258 篇 电气工程
- 174 篇 生物工程
- 146 篇 控制科学与工程
- 119 篇 机械工程
- 107 篇 生物医学工程（可授...
- 56 篇 电子科学与技术（可...
- 48 篇 材料科学与工程（可...
- 48 篇 化学工程与技术
- 47 篇 安全科学与工程
- 37 篇 仪器科学与技术
- 35 篇 交通运输工程
- 23 篇 建筑学
- 23 篇 网络空间安全
543 篇 理学
- 340 篇 物理学
- 194 篇 数学
- 175 篇 生物学
- 57 篇 化学
- 57 篇 统计学（可授理学、...
206 篇 医学
- 195 篇 临床医学
- 60 篇 基础医学(可授医学...
- 45 篇 药学(可授医学、理...
- 23 篇 公共卫生与预防医...
172 篇 管理学
- 96 篇 管理科学与工程(可...
- 78 篇 图书情报与档案管...
- 31 篇 工商管理
28 篇 农学
- 28 篇 作物学
19 篇 法学
17 篇 经济学
7 篇 教育学
5 篇 军事学
1 篇 文学

主题

307 篇 computer vision
228 篇 image processing
102 篇 image segmentati...
92 篇 feature extracti...
70 篇 deep learning
50 篇 object detection
42 篇 cameras
39 篇 image edge detec...
35 篇 convolutional ne...
34 篇 visualization
34 篇 image recognitio...
32 篇 machine learning
32 篇 image reconstruc...
31 篇 neural networks
30 篇 image color anal...
28 篇 computational mo...
28 篇 shape
28 篇 training
26 篇 image analysis
25 篇 face recognition

机构

6 篇 university of ch...
5 篇 comput & inf sci...
5 篇 department of co...
4 篇 chitkara univers...
4 篇 image processing...
4 篇 beijing institut...
4 篇 college of compu...
3 篇 shanghai jiao to...
3 篇 dept of electr &...
3 篇 south china univ...
3 篇 inria sophia-ant...
3 篇 school of comput...
3 篇 department of ma...
3 篇 shenyang institu...
3 篇 department of co...
3 篇 school of electr...
3 篇 school of comput...
3 篇 college of compu...
3 篇 dept of control ...
3 篇 department of in...

作者

6 篇 andayani u.
5 篇 syahputra m.f.
5 篇 mohana
5 篇 siregar b.
4 篇 yang yang
4 篇 hassan ghassemia...
4 篇 li hui
4 篇 shohreh kasaei
4 篇 e.j. delp
3 篇 luo haibo
3 篇 paul joseph sure...
3 篇 wang wei
3 篇 wang qian
3 篇 ying liu
3 篇 maryam imani
3 篇 saurav sumeet
3 篇 weickert joachim
3 篇 muchtar m.a.
3 篇 xin wang
3 篇 aggarwal j.k.

语言

1,615 篇 英文
25 篇 其他
9 篇 中文
1 篇 德文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1646 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：

Comprehensive Review of Various Optimization Algorithms for image Captioning 3

Comprehensive Review of Various Optimization Algorithms for ...

引用

3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Juzer Gabajiwala, Hamza Kamlesh Jethwa, Nishant Joshi, Parth Bimal Anurag Mishra, Arundhati Natu, Prachi MPSTME NMIMS Department of Computer Engineering Mumbai India

ISBN: (纸本)9781665438117

Automatic image captioning is a prominent and challenging research problem in the field of computer vision and natural language processing with applications ranging in biology, automotive and several other domains. Human brain being a complex organ, is able to constantly identify and establish all the intricate details of a scene in real-time, which a machine fails to imitate. However, with neoteric advances in Artificial Intelligence, image captioning has witnessed significant progress. This paper provides a comprehensive review of the latest advancements in caption generation with an emphasis on encoder-decoder and generative adversarial network frameworks. A comparative analysis is performed on existing approaches and methodologies along with an overview of commonly used datasets and evaluation metrics. © 2021 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Arabian date classification using CNN algorithm with various pre-trained models 3

Arabian date classification using CNN algorithm with various...

引用

3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Khayer, Md.Abu Hasan, Md.Sakibul Sattar, Abdus Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9780738111834

The people of Bangladesh have a lack of knowledge to detect fruit so many consumers suffer when they go to buy fruit every day. We know it is very difficult for consumers to detect the class of date on their naked eyes. It is necessary to build a model that can predict the class of a date. We have been select computer vision terminology for properly classifying the data images. Here the random typical fruiting of images in our dataset is used. We used CNN for image classification here and the algorithm provides an optimal architecture through image classification. The CNN's algorithm works well for any image This method will help consumers to identify the dates. We use computer vision and object recognition image processing to create interactive real-world and thereby enhancing the user experience. The main goal of our research is to demonstrate the feasibility and test the effectiveness of the project and the focus of our project is to identify different types of dates and this can help a consumer to get an instant solution to the date fruit identification problem very easily. The validation accuracy proportions of our research contain 82.67%. © 2021 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Modular StoryGAN with Background and Theme Awareness for Story Visualization 3rd

Modular StoryGAN with Background and Theme Awareness for Sto...

引用

3rd international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Szűcs, Gábor Al-Shouha, Modafar Budapest University of Technology and Economics Műegyetem rkp. 3 Budapest1111 Hungary

ISBN: (纸本)9783031090363

Story visualization is a novel topic that intersects computer vision and natural language processing. In this task, given a series of natural language sentences that compose a story, a sequence of images should be generated that correspond to the sentences. Prior works have introduced recurrent generative models which outperform text-to-image models on this task;however, local and global consistency is a challenging attribute of these solutions. For the improvement, we proposed a new modular model architecture named Modular StoryGAN containing the best promising components of prior works. To measure the local and global consistency we introduced background and theme awareness, which are expected attributes of the solutions. Based on the human evaluation, the generated images demonstrate that Modular StoryGAN possesses background and theme awareness. Besides the subjective evaluation, the objective one also shows that our model outperforms the state-of-the-art CP-CSV and DuCo models. © 2022, Springer Nature Switzerland AG.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA images 25th

Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic R...

引用

25th international conference on Medical image Computing and computer-Assisted Intervention , MICCAI 2022

作者： Hou, Junlin Xiao, Fan Xu, Jilan Zhang, Yuejie Zou, Haidong Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Department of Ophthalmology Shanghai General Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031336577

The ultra-wide optical coherence tomography angiography (OCTA) has become an important imaging modality in diabetic retinopathy (DR) diagnosis. However, there are few researches focusing on automatic DR analysis using ultra-wide OCTA. In this paper, we present novel and practical deep-learning solutions based on ultra-wide OCTA for the Diabetic Retinopathy Analysis Challenge (DRAC). In the first task of segmentation of DR lesions, we utilize UNet and UNet++ to segment three lesions with strong data augmentation and model ensemble. In the second task of image quality assessment, we create an ensemble of Inception-V3, SE-ResNeXt, and vision Transformer models. Pre-training on the large dataset as well as the hybrid MixUp and CutMix strategy are both adopted to boost the generalization ability of our models. In the third task of DR grading, we build a vision Transformer and find that the model pre-trained on color fundus images serves as a useful substrate for OCTA images. Extensive ablation studies demonstrate the effectiveness of each designed component in our solutions. The proposed methods rank 4th, 3rd, and 5th on the three leaderboards of DRAC, respectively. Our code is publicly available at https://***/FDU-VTS/DRAC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

引用

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Marjana Tahmid Samiul Alam Kalim Akram Kazi Muhammad Asif Ashrafi Dept. of CSE East Delta University Chittagong Bangladesh Dept. of EEE East Delta University Chittagong Bangladesh Dept. of CSE Ulm University Ulm Germany

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and computer vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages" to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of Diabetic Retinopathy Using image Pre-processing Techniques

Classification of Diabetic Retinopathy Using Image Pre-proce...

引用

Intelligent Technologies (CONIT), international conference on

作者： Kavya Duvvuri Harshitha Kanisettypalli Masabattula Teja Nikhil Suja Palaniswamy Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Most diseases that affect humans cause an effect on the eyes which is observed first by a physician while treating a patient. Eye diseases are conditions that affect the functioning of the eye and lead to loss of vision. Globally, at least 2.2 billion people have near or distant vision impairment. Most of these eye diseases have long-term effects so, early detection of the eye disease is very important to avoid the consequences. Some eye diseases include cataracts, glaucoma, diabetic retinopathy, and retinal detachment, etc. Almost 10% of people are facing an eye disease called diabetic retinopathy (DR). In this paper, multiclassification of DR images has been done after performing a few preprocessing techniques like erosion, histogram equalization, and comparing the accuracy of the CNN model before and after the pre-processing. The classification model yielded an accuracy of 86.4% on retinal fundus images filtered from the Kaggle DR detection database. The obtained results show that the proposed preprocessing methods are best suitable for diagnosing diabetic retinopathy from retinal fundus images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Approaches for Pneumonia Classification in Healthcare

Deep Learning Approaches for Pneumonia Classification in Hea...

引用

Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： Sriram G K Karunasagar A Sudharsan R A Malini Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai India

In the past two decades, there has been a sharp rise in the use of deep learning for medical image processing and analysis. Recent challenges, for instance, the most well-known imageNet computer vision competition, have almost entirely incorporated deep learning approaches for providing the best result. The concept of image classification was later extended to image Segmentation and Object Detection which proved to perform extremely well using state-of-the-art classification algorithms as their backbone architecture. The accuracy of the algorithm and approach has a significant impact on the medical field as there is a constant need for accurate and computationally efficient models. The existing object detection and segmentation approaches need large data for providing accurate results, unlike classification algorithms in which accuracy can be achieved with a relatively smaller amount of data. Hence, for the overall increase of model accuracy, there is a need for image augmentation to be incorporated. In this paper, several deep learning methodologies such as classification, object detection, ensemble, and segmentation for pneumonia classification and detection have been reviewed and an ensemble-based approach for the classification of Pneumonia using chest X-rays has been proposed.

关键词： Deep learning image segmentation Pulmonary diseases Computational modeling Object detection Medical services Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Automatic Generation and Optimization Design of Embroidery Patterns Based on image processing

Automatic Generation and Optimization Design of Embroidery P...

引用

Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Shuang Qiao Beijing Institute of Fashion Technology Beijing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

With the improvement of living environment and the improvement of people's quality of life, embroidery, as a traditional handicraft art, is gradually receiving more attention and love from people. Deep learning (DL) has demonstrated strong capabilities in image generation and processing, particularly in generating visually reasonable and highly detailed images. This provides new opportunities for the automatic generation and optimization design of embroidery patterns. Traditional embroidery pattern generation methods may rely on rules, templates, or manual adjustments, while DL methods can automatically learn complex patterns and structures in images by training large amounts of data, improving the efficiency of embroidery pattern generation and optimization. The DL model can process a large amount of data in a short period of time and automatically generate and optimize patterns. This article designs an automatic generation and optimization algorithm for embroidery patterns based on image processing, using DL technology to learn and generate innovative and artistic embroidery patterns from a large amount of embroidery image data. The experimental results show that the algorithm proposed in this paper can achieve automatic generation and optimization of embroidery patterns, meeting the needs of users for unique and beautiful embroidery patterns.

关键词： Training Deep learning computer vision image synthesis Semantic segmentation Manuals Product design Data models Quality assessment Optimization

来源：评论

学校读者我要写书评

暂无评论

image Captioning Methods and Metrics

Image Captioning Methods and Metrics

引用

3rd IEEE international conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Sargar, Omkar Kinger, Shakti MIT World Peace Univ Comp Dept Pune Maharashtra India

ISBN: (纸本)9781728185194

image Captioning is one of the emerging topics of research in the field of AI. It uses a combination of computer vision (CV) and Natural Language processing (NLP) to derive features from the image, use this information to identify objects, actions, their relationships, and generate a description for the image. It is most important concept in artificial intelligence applied in the fields like aid to the blind, self-driving cars, and many more. This paper we demonstrates a concise state of art image captioning and its method for caption generation using deep learning concepts. We also determine the approach for image caption generation using Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) model in deep learning framework. Using this approach system intelligent enough to create sentences for images. It uses the encoder-decoder architecture, where CNN is used for image vector generation and LSTM is used for the generation of a logical sentence using the NLP concepts. Finally, we evaluate the proposed system experimental analysis with numerous existing systems and show the effeteness of system.

关键词： Deep learning AI natural language processing computer vision

来源：评论

学校读者我要写书评

暂无评论

computer vision for Determining Trajectory and Impact Position of Fighter-Aircraft Bombing

Computer Vision for Determining Trajectory and Impact Positi...

引用

Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： Ardian Infantono Ridi Ferdiana Rudy Hartanto Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta Indonesia

The motion detection process of bombing by fighter aircraft or combat drones is very important in the Weapon Impact Scoring System (WISS). Recently, the available WISS results are only based on the impact position of the fighter-aircraft bomb in the Air Weapon Range (AWR). The combat training safety regulation pushes observation which can only be made away from AWR. This paper presents the technique of computer vision to enhance WISS capability in observing fighter-aircraft bombs in the AWR. In this proposed method, not only the impact position is observed, but also the trajectory of the bomb gliding before exploding on the ground will be captured. This technique uses a digital video camera safely placed far from the AWR. The camera with a certain altitude on the tower is directed to the circle target to the source of the bombing. The proposed method processes the five-points position of the target circle like a clock (12-3-6-9-central point) while observing the bombing process prior to impact in the AWR. Based on real-time video processing, the image processing successfully makes a sequence of bomb trajectory and its impact position quickly. The results are very important to inform the pilot to improve the next bombing and to support precise analysis of more comprehensive bombing training.

关键词：

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165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：