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

25,302 篇 会议
282 篇 期刊文献
21 册 图书
3 篇 学位论文

馆藏范围

25,608 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

15,842 篇 工学
- 9,892 篇 计算机科学与技术...
- 6,083 篇 电气工程
- 5,779 篇 信息与通信工程
- 5,627 篇 软件工程
- 2,035 篇 光学工程
- 1,458 篇 控制科学与工程
- 1,281 篇 机械工程
- 1,157 篇 电子科学与技术（可...
- 877 篇 生物医学工程（可授...
- 834 篇 生物工程
- 799 篇 仪器科学与技术
- 268 篇 网络空间安全
- 253 篇 化学工程与技术
- 248 篇 安全科学与工程
- 243 篇 交通运输工程
- 183 篇 材料科学与工程（可...
- 167 篇 土木工程
- 164 篇 建筑学
5,731 篇 理学
- 3,491 篇 物理学
- 2,208 篇 数学
- 886 篇 生物学
- 566 篇 统计学（可授理学、...
- 419 篇 系统科学
- 317 篇 化学
3,025 篇 医学
- 2,900 篇 临床医学
- 313 篇 基础医学(可授医学...
- 229 篇 药学(可授医学、理...
1,401 篇 管理学
- 856 篇 管理科学与工程(可...
- 618 篇 图书情报与档案管...
- 169 篇 工商管理
181 篇 法学
134 篇 农学
58 篇 教育学
52 篇 文学
51 篇 经济学
51 篇 军事学
22 篇 艺术学

主题

3,123 篇 image processing
2,084 篇 image coding
2,027 篇 visualization
1,754 篇 image segmentati...
1,490 篇 feature extracti...
1,083 篇 image reconstruc...
908 篇 cameras
885 篇 signal processin...
833 篇 image color anal...
756 篇 humans
717 篇 image edge detec...
692 篇 image enhancemen...
667 篇 computer vision
651 篇 training
584 篇 image analysis
575 篇 deep learning
536 篇 image quality
481 篇 conferences
473 篇 object detection
470 篇 robustness

机构

51 篇 school of electr...
49 篇 shanghai jiao to...
39 篇 ieee
38 篇 university of sc...
36 篇 shanghai jiao to...
36 篇 school of comput...
34 篇 shanghai jiao to...
33 篇 university of ch...
32 篇 microsoft resear...
26 篇 national institu...
25 篇 department of el...
24 篇 hendisli&#x011f
23 篇 institute for in...
23 篇 institute of ima...
23 篇 istanbul teknik ...
23 篇 institute of dig...
22 篇 peking univ inst...
21 篇 institute of inf...
21 篇 univ chinese aca...
21 篇 univ sci & techn...

作者

62 篇 guangtao zhai
46 篇 song li
45 篇 zhai guangtao
32 篇 jie yang
27 篇 li li
25 篇 m. vetterli
25 篇 bovik alan c.
25 篇 li sumei
25 篇 li song
25 篇 sarp ertürk
24 篇 jing zhang
24 篇 b. macq
23 篇 zhang lei
23 篇 li zhuo
23 篇 d.r. bull
22 篇 jürgen seiler
21 篇 shi guangming
20 篇 liu yang
20 篇 zhang wenjun
18 篇 mohamed-chaker l...

语言

24,610 篇 英文
489 篇 土耳其文
394 篇 其他
137 篇 中文
2 篇 西班牙文
2 篇 葡萄牙文

检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

共 25608 条记录，以下是4521-4530 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：

ANALYZING SENSOR QUANTIZATION OF RAW imageS FOR visual SLAM

ANALYZING SENSOR QUANTIZATION OF RAW IMAGES FOR VISUAL SLAM

引用

ieee International conference on image processing (ICIP)

作者： Christie, Olivia Rego, Joshua Jayasuriya, Suren Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Sch Arts Media & Engn Tempe AZ USA

ISBN: (纸本)9781728163956

visual simultaneous localization and mapping (SLAM) is an emerging technology that enables low-power devices with a single camera to perform robotic navigation. However, most visual SLAM algorithms are tuned for images produced through the image sensor processing (ISP) pipeline optimized for highly aesthetic photography. In this paper, we investigate the feasibility of varying sensor quantization on RAW images directly from the sensor to save energy for visual SLAM. In particular, we compare linear and logarithmic image quantization and show visual SLAM is robust to the latter. Further, we introduce a new gradient-based image quantization scheme that outperforms logarithmic quantization's energy savings while preserving accuracy for feature-based visual SLAM algorithms. This work opens a new direction in energy-efficient image sensing for SLAM in the future.

关键词： visual SLAM image sensor quantization RAW images embedded computer vision

来源：评论

学校读者我要写书评

暂无评论

IN-BED PRESSURE-BASED POSE ESTIMATION USING image SPACE REPRESENTATION LEARNING

IN-BED PRESSURE-BASED POSE ESTIMATION USING IMAGE SPACE REPR...

引用

ieee International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Davoodnia, Vandad Ghorbani, Saeed Etemad, Ali Queens Univ Dept Elect & Comp Engn Kingston ON Canada York Univ Dept Elect Engn & Comp Sci Toronto ON Canada

ISBN: (纸本)9781728176055

Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data for subsequent pose estimation. Our model transforms the ambiguous pressure maps to images containing shapes and structures similar to the common input domain of the preexisting pose estimation methods. As a result, we show that our model is able to reconstruct unclear body parts, which in turn enables pose estimators to accurately and robustly estimate the pose. We train and test our method on a manually annotated public pressure map dataset using a combination of loss functions. Results confirm the effectiveness of our method by the high visual quality in the generated images and the high pose estimation rates achieved.

关键词： In-Bed Pose Estimation Smart Beds Pre-processing Deep learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Feature Extraction Improves image Retrieval by Fusing Diverse Methods for Enhanced Content-Based Search

Hybrid Feature Extraction Improves Image Retrieval by Fusing...

引用

Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： B. Buvaneswari Zainab Alassedi Gotte Ranjith kumar Mohammed I. Habelalmateen Ghazi Mohamad Ramadan Department of Information Technology Panimalar Engineering College Chennai India National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India The Islamic university Najaf Iraq College of MLT Ahl Al Bayt University Karbala Iraq

Content-based image retrieval (CBIR) is a critical domain in computer vision, dedicated to extracting images from databases based on their visual content rather than relying on text-based queries. Feature extraction plays a pivotal role in CBIR, converting image attributes like color, texture, and shape into numerical representations, facilitating efficient image matching. It lacks a comprehensive exploration of the distinct impacts of fusion methods and often falls short in detailing feature extraction architectures. This paper introduces a novel approach to tackle the proposing a hybrid feature extraction method. The method evaluates its performance using the Corel 1K dataset, a collection of 1,000 images spanning diverse categories, serving as a benchmark for assessing the efficacy of content-based image retrieval techniques in real-world scenarios. The results achieved by the proposed hybrid feature extraction method surpass those of existing models, with impressive precision (0.956), recall (0.872), and F -measure (0.892). This method is compared to the KMFO model and performance evaluation in CBIR using the SVM model. Future research is focus on refining and optimizing advanced feature extraction techniques to enhance the efficiency and effectiveness of CBIR systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Seeing Clear before visual Tracking

Seeing Clear before Visual Tracking

引用

International conference on Computer and communications (ICCC)

作者： Ximing Zhang Yuanbo Wang Hui Zhao Xuewu Fan Space optical research lab Xi'an Institute of Optics and Precision Mechanics of CAS) Xi'an China

In this paper, we propose a two-stages visual tracking method mainly based on two branches including image deblurring and visual tracking. Our main motivation is to achieve the robust visual tracking when the tracker is suffering fast motion blur. Firstly, we present the hierarchical model based on Spatial Pyramid Matching that performs the fine-to-coarse deblurring and exploits localized-to-coarse operations. After achieving the deblurred images, the proposed method use transformer framework with spatial and channel attention for extracting features in order to obtain the spatial and channel features simultaneously to obtain the fast visual tracking with the balance of accuracy and robustness. We first train the one-stage deblurring network in the dataset of Gopro. Then, we train the second stage visusal tracking branch. Lastly, we conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the outperforming results on large tracking benchmarks, we also validate the effectiveness of our method against the fast motion blurring.

关键词： visualization Tracking Benchmark testing Feature extraction Transformers Robustness image restoration

来源：评论

学校读者我要写书评

暂无评论

image Correction for Improving visual Acuity Using Zernike-Based Vision Simulation 20

Image Correction for Improving Visual Acuity Using Zernike-B...

引用

20th International Symposium on communications and Information Technologies (ISCIT)

作者： Tanaka, Hiromu Kawano, Hideaki Kyushu Inst Technol Grad Sch Engn Kitakyushu Fukuoka Japan Kyushu Inst Technol Fac Engn Kitakyushu Fukuoka Japan

ISBN: (纸本)9781665449588

This paper proposes a novel image correction method to improve visions of individuals with refractive errors such as myopia, hyperopia, and astigmatism. Refractive errors can be corrected by eyeglasses, contact lenses, or surgery, but there are problems such as maintenance or cost. Our method corrects images so that the corrected images are perceived similarly to the original images without eyeglasses. Our method consists of two models: image correction model and blur simulation model. The image correction model is a convolutional neural network model designed for super-resolution tasks, and the blur simulation model simulates the perceived blur of a human eye. Perceived blur can be modeled by Zernike polynomials-based point spread function (PSF), and blurred images are computed by convolving the images with PSF. Our model works as follows. First, images are fed to the image correction model to produce corrected images. Then, these corrected images are passed to the blur simulation model. The image correction model is trained so that blurred corrected images become identical to the original images. After training is completed, only the first step is required to correct images. Our approach can correct any type of image using the same model without significant contrast loss.

关键词： image processing image correction convolutional neural network Zernike polynomials

来源：评论

学校读者我要写书评

暂无评论

Improving Absorption Properties of Nanoparticles Using Self-Similar Array 4

Improving Absorption Properties of Nanoparticles Using Self-...

引用

4th International conference on communications, Signal processing, and their Applications, ICCSPA 2020

作者： Abougharbia, Rozaina Nasser, A.A.A. Morsi, Iman Aly, Moustafa H. Electronics and Communications Department Arab Academy for Science Technology & Maritime Transport Alexandria Egypt

ISBN: (纸本)9781728165356

In photo-thermal therapy, the localization of heat effects to cancer cells is an over growing approach in cancer treatment. This potentially reduces the side effects on tumor neighboring cells, improves the quality of life of the patient, and generally enhances the effectiveness of the hyperthermia. The optical properties of the nanoparticles depend mainly on their shape and size. The aim of this work is to enhance the photo thermal properties of noble metal nanostructures through improving their absorption characteristics. This is achieved using new shapes as well as new geometrical configurations of self-similar arrays of nanoparticles. © 2020 ieee

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Study on the Segmentation and Classification of Diabetic Retinopathy images Using the K-Means Clustering Method

A Study on the Segmentation and Classification of Diabetic R...

引用

ieee Signal processing and communications Applications (SIU)

作者： Ramazan İncir Ferhat Bozkurt Gümüşhane Üniversitesi Atatürk Üniversitesi Gümüşhane Türkiye Bilgisayar Teknolojileri Bölümü Bilgisayar Mühendisliği Bölümü Erzurum Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Diabetic Retinopathy (DR) is a retinal disease caused by diabetes, representing one of the most prevalent causes of vision loss affecting millions of people worldwide. Swift detection and treatment of this condition are crucial for preventing the disease. Various deep learning and machine learning algorithms have been employed for disease detection and classification, often overlooking the data preprocessing stage. In the data preprocessing phase of this study, segmentation of important lesions, such as hard exudates, was conducted using the K-Means clustering method. The identified lesions were highlighted on the original images. The resulting dataset was then classified using pre-trained architectures, namely EfficientNetV2-M, ResNet50, MobileNet, and DenseNet121. After training on the APTOS dataset, the EfficientNetV2-M model achieved an accuracy of 95.16%. The classification results indicated the contribution of the lesion highlighting process during data preprocessing to the overall classification accuracy

关键词： Training image segmentation Diabetic retinopathy Accuracy Clustering methods Data preprocessing Signal processing

来源：评论

学校读者我要写书评

暂无评论

DISTANCE RESTRICTED TRANSFORMER ENCODER FOR MULTI-LABEL CLASSIFICATION

DISTANCE RESTRICTED TRANSFORMER ENCODER FOR MULTI-LABEL CLAS...

引用

2021 ieee International conference on Multimedia and Expo, ICME 2021

作者： Wang, Xiaomei Li, Yaqian Luo, Tong Guo, Yandong Fu, Yanwei Xue, Xiangyang School of Computer Science Fudan University Shanghai China OPPO Research Institute Shanghai China The School of Data Science MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665438643

Multi-label image classification is a fundamental but challenging task in Multimedia *** aims to predict a set of labels presented in an image. Great progress has been made by exploring convolutional neural network with binary cross-entropy loss recently. However, conventional approaches are limited to highlight the key visual contents associated with target labels and pay little attention to confining the distances between visual and positive/negative label representations. To target these aspects, we firstly introduce a variant transformer encoder model for acquiring the underlying and crucial visual information related to ground truth labels. Specifically, a novel primal feature guided net is designed to maintain the original visual features during encoding process. Secondly, we exploit a distance restricted learning strategy in a common semantic space to shrink the distances of images with positive labels while expand with the negative ones during training stage. Extensive experiments are executed on MSCOCO and WIDER Attribute datasets and outstanding performance is achieved compared with other state-of-the-art models. © 2021 ieee

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Leveraging Convolutional Models as Backbone for Medical visual Question Answering

Leveraging Convolutional Models as Backbone for Medical Visu...

引用

ieee International conference on Bioinformatics and Biomedicine (BIBM)

作者： Lei Liu Xiangdong Su Guanglai Gao National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Convolutional neural networks (CNNs) have made significant contributions to computer vision and offer the advantages of higher training efficiency and lower model complexity. However, their application as the backbone in medical visual question answering (MedVQA) remains an open question. To address this issue, we employ popular convolutional models, including ResNet, DenseNet, and ShuffleNet, as the foundation for MedVQA, achieving outstanding performance. Different backbones can be tailored to diverse real-world scenarios. The central challenge in utilizing CNNs for visual question answering is effectively managing textual features and integrating multi-modal information. To overcome this challenge, we design a novel global interaction attention (GIA) that facilitates efficient interactions between text and image features. Additionally, we utilize the dot product before the classifier output to enhance visual and textual modal fusions. To further enhance model performance, we propose a novel multi-modal hidden mixup (MHidMix) technique for data augmentation, which involves interpolating hidden states during model training. This data augmentation technique smoothes the decision boundary without the need for complex sample selection, further improving model performance. Experimental results underscore the versatility of our proposed framework across various convolutional models, leading to outstanding performance on four MedVQA datasets. Notably, we achieved an accuracy increase of 9.4% on the PathVQA dataset and 4.5% on the OVQA dataset.

关键词： Training visualization Interpolation Computational modeling Biological system modeling Feature extraction Data augmentation Question answering (information retrieval) Data models Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

A Deep Pyramid Attention Network for Single image Super-resolution 9

A Deep Pyramid Attention Network for Single Image Super-reso...

引用

9th International Japan-Africa conference on Electronics, communications, and Computations, JAC-ECC 2021

作者： Gendy, Garas Mohammed, Hazem Sabor, Nabil He, Guanghui Shanghai Jiao Tong University Department of Micro-Nano Electronics Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut Egypt Assiut University Faculty of Engineering Electrical Engineering Department Assiut Egypt

ISBN: (纸本)9781665482929

The pyramid attention (PA) network is a new structure developed for digital image processing. This network was designed to extract long-range features at different locations and scales. Recently, PA architecture has been introduced in single image super-Resolution (SISR) to improve the model's ability to benefit from data's self-similarity. However, the effects of location and number of PA on extracting the self-similarity are not explored. In this paper, a Deep Pyramid Attention Network (DPANet) is proposed for SISR based on exploring the PA block. This is performed by studying the effect of varying the number of PA blocks and their locations on the model performance. Moreover, the effect of the residual scale on the PA's performance is studied. Evaluated based on five benchmark datasets, we concluded that using five PA blocks without down-scale residual interchanging with Resblocks in the network achieves significantly better results compared to the state-of-the-art methods. In addition, our model achieves superior visual quality and accuracy. © 2021 ieee.

关键词： Location

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 << < 449 450 451 452 453 454 455 456 457 458 > >>

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：