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

13,890 篇 会议
856 篇 期刊文献
31 册 图书
29 篇 学位论文
3 篇 专利

馆藏范围

14,808 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

7,807 篇 工学
- 4,071 篇 计算机科学与技术...
- 2,274 篇 电气工程
- 2,009 篇 软件工程
- 1,274 篇 生物医学工程（可授...
- 1,054 篇 信息与通信工程
- 991 篇 光学工程
- 659 篇 控制科学与工程
- 640 篇 仪器科学与技术
- 626 篇 核科学与技术
- 571 篇 机械工程
- 440 篇 电子科学与技术（可...
- 401 篇 生物工程
- 238 篇 测绘科学与技术
- 156 篇 材料科学与工程（可...
- 116 篇 航空宇航科学与技...
- 113 篇 建筑学
- 113 篇 土木工程
3,763 篇 理学
- 2,655 篇 物理学
- 1,105 篇 数学
- 469 篇 生物学
- 316 篇 统计学（可授理学、...
- 184 篇 化学
- 165 篇 系统科学
2,631 篇 医学
- 1,608 篇 临床医学
- 1,261 篇 特种医学
- 265 篇 基础医学(可授医学...
- 114 篇 医学技术（可授医学...
- 108 篇 药学(可授医学、理...
393 篇 管理学
- 271 篇 管理科学与工程(可...
- 137 篇 图书情报与档案管...
72 篇 农学
37 篇 艺术学
35 篇 法学
22 篇 经济学
21 篇 教育学
21 篇 文学
13 篇 军事学
3 篇 历史学

主题

7,686 篇 image reconstruc...
1,032 篇 cameras
874 篇 three-dimensiona...
784 篇 computed tomogra...
714 篇 image segmentati...
621 篇 reconstruction a...
611 篇 positron emissio...
561 篇 surface reconstr...
547 篇 shape
516 篇 image resolution
466 篇 training
464 篇 magnetic resonan...
456 篇 data mining
375 篇 imaging phantoms
363 篇 detectors
359 篇 deep learning
355 篇 image coding
348 篇 solid modeling
333 篇 attenuation
333 篇 compressed sensi...

机构

91 篇 univ chicago dep...
33 篇 department of ra...
32 篇 tsinghua univ de...
29 篇 department of ra...
28 篇 department of ra...
24 篇 suny stony brook...
22 篇 dartmouth coll t...
21 篇 microsoft resear...
20 篇 univ chinese aca...
20 篇 department of en...
18 篇 univ chicago dep...
17 篇 washington univ ...
16 篇 shanghai jiao to...
15 篇 university of sc...
15 篇 institute of nuc...
15 篇 university of ch...
14 篇 department of ra...
14 篇 tsinghua univ mi...
14 篇 department of ra...
13 篇 tsinghua univers...

作者

84 篇 pan xiaochuan
33 篇 xia dan
26 篇 emil y. sidky
25 篇 kudo hiroyuki
25 篇 liang zhengrong
24 篇 m. defrise
23 篇 li zhang
23 篇 m.a. king
20 篇 zhengrong liang
20 篇 maier andreas
20 篇 ma jianhua
19 篇 kris thielemans
18 篇 yuxiang xing
18 篇 j.a. fessler
18 篇 g.t. gullberg
17 篇 grant t. gullber...
17 篇 liu yan
17 篇 chen guang-hong
16 篇 hutton brian f.
16 篇 chen buxin

语言

14,473 篇 英文
204 篇 其他
122 篇 中文
11 篇 土耳其文
2 篇 俄文
1 篇 德文
1 篇 日文
1 篇 泰文
1 篇 乌克兰文

检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

共 14809 条记录，以下是481-490 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：

Field weed classification based on modified LBP and local sparse representation

Field weed classification based on modified LBP and local sp...

引用

2024 International conference on image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Yang, Huan College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Accurately identifying field weeds is crucial for selecting appropriate agricultural machinery and herbicides. This paper focuses on nine common weed species during the seedling stage in natural backgrounds of field vegetables, proposing a novel weed foreground segmentation method based on image processing and a new weed classification model using sparse representation classification (K_SRC). Initially, K-means clustering and threshold segmentation are applied to the a component of Lab color space to obtain binary weed images, followed by area filtering to remove interference. The binary weed image is then masked with the original image to extract clean weed images. Color features and mLBP texture features are extracted from segmented weed images to form a training sample set. KNN computes a local dictionary, and sparse coefficients are calculated using the OMP algorithm. Experimental results demonstrate a classification accuracy of 95.22% compared to four other algorithms. Additionally, this research provides new insights into the application of sparse representation in agriculture. © 2024 SPIE.

关键词： image segmentation Education and training Feature extraction image classification Detection and tracking algorithms Binary data image processing algorithms and systems reconstruction algorithms image processing

来源：评论

学校读者我要写书评

暂无评论

Towards L-System Captioning for Tree reconstruction

Towards L-System Captioning for Tree Reconstruction

引用

44th Annual conference on European Association for Computer Graphics, EUROGRAPHICS 2023

作者： Magnusson, J.S. Hilsmann, A. Eisert, P. Fraunhofer Heinrich Hertz Institute HHI Germany Institut für Informatik Humboldt-University Germany

ISBN: (纸本)9783038682097

This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach. We train a model end-to-end which is able to translate given images into L-System words as a description of the displayed tree. To prove this concept, we demonstrate the applicability on 2D tree topologies. Transferred to real image data, this novel idea could lead to more efficient, accurate and semantically meaningful tree and plant reconstruction without using error-prone point cloud extraction, and other processes usually utilized in tree reconstruction. Furthermore, this approach bypasses the need for a predefined L-System grammar and enables species-specific L-System inference without biological knowledge. © 2023 Authors.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

TopFusion: Using Topological Feature Space for Fusion and Imputation in Multi-Modal data

TopFusion: Using Topological Feature Space for Fusion and Im...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Myers, Audun Kvinge, Henry Emerson, Tegan Pacific Northwest Natl Lab Math Data Sci Richland WA USA Univ Washington Dept Math Seattle WA USA Colorado State Univ Dept Math Ft Collins CO USA Univ Texas El Paso Dept Math Sci El Paso TX USA

ISBN: (纸本)9798350302493

We present a novel multi-modal data fusion technique using topological features. The method, TopFusion, leverages the flexibility of topological data analysis tools (namely persistent homology and persistence images) to map multi-modal datasets into a common feature space by forming a new multi-channel persistence image. Each channel in the image is representative of a view of the data from a modality-dependent filtration. We demonstrate that the topological perspective we take allows for more effective data reconstruction, i.e. imputation. In particular, by performing imputation in topological feature space we are able to outperform the same imputation techniques applied to raw data or alternatively derived features. We show that TopFusion representations can be used as input to downstream deep learning-based computer vision models and doing so achieves comparable performance to other fusion methods for classification on two multi-modal datasets.

关键词： Modal analysis

来源：评论

学校读者我要写书评

暂无评论

Design of Face image Super-Resolution Network Based on Multi-scale Perceptive Inception Dense Block 11

Design of Face Image Super-Resolution Network Based on Multi...

引用

11th Joint International Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Liu, Jiayu Qu, Xiujie He, Xinyan Zhu, Qiang School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9798350333664

Within the realm of image data manipulation, enhancing resolution and reconstructing facial visuals stand as pivotal methods designed to restore poorly defined images impacted by unidentified deterioration mechanisms. Traditional non-generative adversarial network (non-GAN) models suffer from high-frequency information reconstruction deficiencies when restoring high-frequency information in images. Utilizing the adversarial learning process between generators and discriminators, GANs are capable of synthesizing high-definition imagery enriched with authentic texture and enhanced clarity. However, they have inherent limitations in terms of pixel-level changes, line distortions and detail recovery. In the context of Mixed Residual of Multi-scale Perceptive Inception Dense Block GAN (MRMPIDBGAN), a Multi-scale Perceptive Inception Block (MPIB) is engineered for the robust retrieval of features and multi-scale data from facial images. Based on MPIB, a Mixed Residual of Multi-scale Perceptive Inception Dense Block (MRMPIDB) is further constructed to form the generator, which facilitates better capture and reconstruction of facial image details. Secondly, to more accurately capture the local texture information of the image, the original VGG discriminator in ESRGAN is replaced by a U-Net discriminator, and spectral normalization techniques are applied to improve the stability of the discriminator. Subsequently, an innovative upsampling technique is formulated to optimize efficacy yet minimize computational demands. Further improvements in the model's efficacy are achieved through the replacement of the traditional VGG loss by a metric grounded in perceptual criteria. Experimental results confirm the effectiveness of this algorithm in super-resolution facial image restoration and demonstrate significant advantages in facial image reconstruction, clear texture capture and feature detailing compared to existing methods. © 2023 IEEE.

关键词： facial image enhancement GANs hybrid upsampling perceptual metrics

来源：评论

学校读者我要写书评

暂无评论

A Green Learning Approach to Efficient image Demosaicking

A Green Learning Approach to Efficient Image Demosaicking

引用

2024 IEEE International conference on Big data, Bigdata 2024

作者： Movahhedrad, Mahtab Chen, Zijing Kuo, C.-C. Jay University of Southern California Los AngelesCA United States

ISBN: (纸本)9798350362480

Demosaicking is a critical process in the digital imaging pipeline, tasked with reconstructing full-color images from sampled data captured by R/G/B color sensors. The challenge arises from two-thirds of the pixel data being missing, which complicates the task of accurate reconstruction. Recent deep learning-based solutions have yielded considerable advancements in demosaicking performance. However, they are computationally intensive and rely on large model architectures, rendering them unsuitable for edge-devices deployment. This work introduces a novel demosaicking method to address these challenges based on green learning (GL), named green image demosaicking (GID). GID offers model transparency while significantly reducing the model size and computational complexity against deep learning methods. Notably, GID does not utilize neural networks. Instead, it is built upon unsupervised representation learning and supervised feature dimension reduction. GID effectively addresses the challenges of big data in vision applications and enhances predictive accuracy during decision-making. GID is engineered for rapid execution with parallel training, making it well-suited for real-time vision tasks on resource-constrained devices. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Ground-Guided Conditional Pixel Synthesizer for Height-based Satellite imagery Super-Resolution

Ground-Guided Conditional Pixel Synthesizer for Height-based...

引用

International Joint conference on Neural Networks (IJCNN)

作者： Xu, Yang Huang, Kejie Yan, Chenggang Tsinghua Univ Beijing 100084 Peoples R China Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Remote sensing plays a crucial role in various fields. However, challenges associated with acquiring high-resolution data from satellite cameras significantly limit their practical applications. The high semantic density per pixel in satellite images makes it challenging for existing methods to extract adequate geometric and semantic information from extremely low-resolution inputs for super-resolution reconstruction. This paper introduces a satellite imagery super-resolution architecture guided by ground-view images, framing the problem as neural pixel synthesis with satellite camera height as a variable factor. This approach proposes a hypergraph-based cross-view mapper module that achieves low-order geometric registration and highorder feature fusion by capturing cross-view visual correlations, accompanied by a height-based pixel synthesizer for continuous multi-level super-resolution, conceptualized as neural rendering. Furthermore, we have developed a multi-level resolution satellite image dataset, complete with ground images from corresponding locations. Extensive experiments on diverse datasets validate the effectiveness of our proposed method in a range of application scenarios.

关键词： Satellite imagery super-resolution Conditional pixel synthesis image generation Neural rendering Geolocalization

来源：评论

学校读者我要写书评

暂无评论

Sparse Points to Dense Clouds: Enhancing 3D Detection with Limited LiDAR data

Sparse Points to Dense Clouds: Enhancing 3D Detection with L...

引用

2024 International conference on Intelligent Robots and Systems

作者： Kumar, Aakash Chen, Chen Mian, Ajmal Lobo, Neils Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Western Australia Perth WA Australia

ISBN: (纸本)9798350377712;9798350377705

3D detection is a critical task that enables machines to identify and locate objects in three-dimensional space. It has a broad range of applications in several fields, including autonomous driving, robotics and augmented reality. Monocular 3D detection is attractive as it requires only a single camera, however, it lacks the accuracy and robustness required for real world applications. High resolution LiDAR on the other hand, can be expensive and lead to interference problems in heavy traffic given their active transmissions. We propose a balanced approach that combines the advantages of monocular and point cloud-based 3D detection. Our method requires only a small number of 3D points, that can be obtained from a low-cost, low-resolution sensor. Specifically, we use only 512 points, which is just 1% of a full LiDAR frame in the KITTI dataset. Our method reconstructs a complete 3D point cloud from this limited 3D information combined with a single image. The reconstructed 3D point cloud and corresponding image can be used by any multi-modal off-the-shelf detector for 3D object detection. By using the proposed network architecture with an off-the-shelf multi-modal 3D detector, the accuracy of 3D detection improves by 20% compared to the state-of-the-art monocular detection methods and 6% to 9% compare to the baseline multi-modal methods on KITTI and JackRabbot datasets.

关键词： image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Self-supervised learning based on StyleGAN for medical image classification on small labeled dataset

Self-supervised learning based on StyleGAN for medical image...

引用

conference on Medical Imaging - image Processing

作者： Fan, Zong Wang, Zhimin Zhang, Chaojie Ozbey, Muzaffer Villa, Umberto Hao, Yao Zhang, Zhongwei Wang, Xiaowei Lia, Hua Univ Illinois Dept Bioengn Urbana IL 61820 USA Univ Illinois Dept Elect & Comp Eng Urbana IL USA Univ Texas Austin Oden Inst Austin TX USA Washington Univ St Louis Dept Radiat Oncol St Louis MO 63130 USA Washington Univ St Louis Dept Radiol St Louis MO USA Univ Illinois Dept Pharmacol & Regenerat Med Chicago IL USA Univ Illinois Canc Ctr Illinois Chicago IL 61820 USA

ISBN: (纸本)9781510671577;9781510671560

Medical image classification plays a vital role in disease diagnosis, tumor staging, and various clinical applications. Deep learning (DL) methods have become increasingly popular for medical image classification. However, medical images have unique characteristics that pose challenges for training DL-based models, including limited annotated data, imbalanced distribution of classes, and large variations in lesion structures. Self-supervised learning (SSL) methods have emerged as a promising solution to alleviate these issues through directly learning useful representations from large-scale unlabeled data. In this study, a new generative self-supervised learning method based on the StyleGAN generator is proposed for medical image classification. The style generator, pre-trained on large-scale unlabeled data, is integrated into the classification framework to effectively extract style features that encapsulate essential semantic information from input images through image reconstruction. The extracted style feature serves as an auxiliary regularization term to leverage knowledge learned from unlabeled data to support the training of the classification network and enhance model performance. To enable efficient feature fusion, a self-attention module is designed for this integration of the style generator and classification framework, dynamically focusing on important feature elements related to classification performance. Additionally, a sequential training strategy is designed to train the classification model on a limited number of labeled images while leveraging large-scale unlabeled data to improve classification performance. The experimental results on a chest X-ray image dataset demonstrate superior classification performance and robustness compared to traditional DL-based methods. The effectiveness and potential of the model were discussed as well.

关键词： Education and training data modeling image classification Medical imaging Feature extraction Performance modeling Solid state lighting Classification systems Design Network architectures

来源：评论

学校读者我要写书评

暂无评论

reconstruction of a SiC-SiC CMC Microstructure Using Deep Learning and Advanced image Processing Technique 38

Reconstruction of a SiC-SiC CMC Microstructure Using Deep Le...

引用

38th Technical conference of the American Society for Composites, ASC 2023

作者： Kaleel, Ibrahim Rauscher, Sophie-Maria Raina, Arun Am Technologiezentrum 5 Augsburg86159 Germany

ISBN: (纸本)9781605956916

The paper presents a workflow for the reconstruction of a SiC-SiC ceramic matrix composite (CMC) microstructure using advanced image processing techniques and deep learning. The objective of this research is to develop highly accurate physics-based computational models for CMCs by gaining a comprehensive understanding of the microstructural features and their impact on material properties. A workflow is presented to classify voxels into individual components and extract stochastic data for establishing microstructure-property correlations. X-ray computed tomography (CT) data of the SiC/SiC CMC with a plain weave architecture and 00/900 fiber orientation are reconstructed using advanced image processing techniques. The resulting CT data is successfully segmented into three material constituents: tows, matrix, and pores. Pore segmentation is accomplished using the Otsu segmentation algorithm, while a deep learning-based U-net model is employed for accurate segmentation between SiC tows and the SiC matrix. Furthermore, an anisotropic segmentation algorithm is utilized to classify tow voxels along different directions, capturing the intricate variations within the microstructure. Geometrical and morphological attributes are extracted from the segmented data for further analysis. © 2023 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Silicon carbide

来源：评论

学校读者我要写书评

暂无评论

Magnetic resonance image reconstruction based on two-stage progressive network 3

Magnetic resonance image reconstruction based on two-stage p...

引用

3rd International conference on Electronic Information Engineering and Computer Communication, EIECC 2023

作者： Shen, Lingyun Zhang, Xiaozhi Xiong, Dongping Ouyang, Lijun School of Electrical Engineering University of South China Hengyang China School of Computing/Software University of South China Hengyang China

ISBN: (纸本)9798350359961

In clinical applications, medical image reconstruction is crucial for extracting complementary information and restoring image quality. However, existing deep learning-based reconstruction methods suffer from the following two problems: first, they ignore the frequency-domain information specific to magnetic resonance imaging, and second, most of the traditional networks are single-end-To-end networks, which lack effective attention to feature information. We suggest a unique two-stage progressive network (TSPNet) to solve these issues. Our approach employs two lightweight Unets as subnetworks for progressive recovery, and the network parts are carefully designed. During the feature extraction phase, we creatively create a Dual Domain Feature Extraction Module (DDFEM) to effectively extract rich features. Subsequently, we create a hybrid attention module (HAM) to effectively process and integrate the retrieved data. Experimental results show that our model outperforms other state-of-The-Art methods and reconstructs images with stronger texture information and superior visual quality. © 2023 IEEE.

关键词： Convolutional neural network deep learning Fourier Transform Medical image reconstruction

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 << < 45 46 47 48 49 50 51 52 53 54 > >>

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：