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

13,848 篇 会议
852 篇 期刊文献
31 册 图书
29 篇 学位论文
3 篇 专利

馆藏范围

14,762 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

7,769 篇 工学
- 4,050 篇 计算机科学与技术...
- 2,266 篇 电气工程
- 2,007 篇 软件工程
- 1,253 篇 生物医学工程（可授...
- 1,050 篇 信息与通信工程
- 989 篇 光学工程
- 652 篇 控制科学与工程
- 639 篇 仪器科学与技术
- 626 篇 核科学与技术
- 569 篇 机械工程
- 434 篇 电子科学与技术（可...
- 401 篇 生物工程
- 237 篇 测绘科学与技术
- 150 篇 材料科学与工程（可...
- 116 篇 航空宇航科学与技...
- 112 篇 建筑学
- 112 篇 土木工程
3,753 篇 理学
- 2,647 篇 物理学
- 1,102 篇 数学
- 469 篇 生物学
- 316 篇 统计学（可授理学、...
- 180 篇 化学
- 164 篇 系统科学
2,610 篇 医学
- 1,602 篇 临床医学
- 1,247 篇 特种医学
- 258 篇 基础医学(可授医学...
- 109 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

7,673 篇 image reconstruc...
1,030 篇 cameras
872 篇 three-dimensiona...
784 篇 computed tomogra...
714 篇 image segmentati...
619 篇 reconstruction a...
606 篇 positron emissio...
561 篇 surface reconstr...
547 篇 shape
516 篇 image resolution
465 篇 training
462 篇 magnetic resonan...
456 篇 data mining
375 篇 imaging phantoms
363 篇 detectors
357 篇 deep learning
354 篇 image coding
346 篇 solid modeling
333 篇 attenuation
332 篇 compressed sensi...

机构

89 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...
17 篇 washington univ ...
17 篇 univ chicago dep...
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

82 篇 pan xiaochuan
31 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

14,489 篇 英文
144 篇 其他
121 篇 中文
11 篇 土耳其文
2 篇 德文
2 篇 俄文
1 篇 法文
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 14763 条记录，以下是201-210 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：

Enhanced three-dimensional reconstruction by photometric stereo 5

Enhanced three-dimensional reconstruction by photometric ste...

引用

5th International conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Abada, Lyes Hannachi, Islam Laallam, Mohamed Wail Aouat, Saliha Bab-Ezzouar Artificial Intelligence and Data Science Department Algiers Algeria

ISBN: (纸本)9798350381450

Computer vision is a very wide field, although, research is advancing rapidly. But there are still many incomplete areas of research. For this reason we are interested on 3D reconstruction field, which consists of creating a 3D object from one or more 2D images. In this paper, we propose two fully convolutional deep networks. These architectures are a combination between the PS-FCN model and the residual neural network architecture. Our two proposed architectures are improvements of an existing work (PS-FCN). We take as input an arbitrary number of 2D images of an object captured from the same camera position and each image is taken with a different light source position. As output they predict the needle map of the object. According to our study on datasets with different training parameters we show that our proposal outperforms the PS-FCN approach which is considered as a very powerful existing technique and gives effective results to solve the photometric stereo problem. © 2023 IEEE.

关键词： Singular value decomposition

来源：评论

学校读者我要写书评

暂无评论

3D Face Tracking from 2D Video through Iterative Dense UV to image Flow

3D Face Tracking from 2D Video through Iterative Dense UV to...

引用

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

作者： Taubner, Felix Raina, Prashant Tuli, Mathieu Teh, Eu Wern Lee, Chul Huang, Jinmiao LG Elect Seoul South Korea

ISBN: (纸本)9798350353013;9798350353006

When working with 3D facial data, improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods are expensive and due to the widespread availability of 2D videos, recent methods have focused on how to perform monocular 3D face tracking. However, these methods often fall short in capturing precise facial movements due to limitations in their network architecture, training, and evaluation processes. Addressing these challenges, we propose a novel face tracker, FlowFace, that introduces an innovative 2D alignment network for dense pervertex alignment. Unlike prior work, FlowFace is trained on high-quality 3D scan annotations rather than weak supervision or synthetic data. Our 3D model fitting module jointly fits a 3D face model from one or many observations, integrating existing neutral shape priors for enhanced identity and expression disentanglement and per-vertex deformations for detailed facial feature reconstruction. Additionally, we propose a novel metric and benchmark for assessing tracking accuracy. Our method exhibits superior performance on both custom and publicly available benchmarks. We further validate the effectiveness of our tracker by generating high-quality 3D data from 2D videos, which leads to performance gains on downstream tasks.

关键词： 3D Face reconstruction 3D reconstruction Digital Human Face Tracking

来源：评论

学校读者我要写书评

暂无评论

CALFNet: A Light Field reconstruction Method Based On Channel Attention Mechanism 16

CALFNet: A Light Field Reconstruction Method Based On Channe...

引用

2024 16th International conference on Graphics and image Processing, ICGIP 2024

作者： Gao, Yuxuan Chen, Zhihong Zhang, Haiwei School of Integrated Circuit Science and Engineering Tianjin University of Technology China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Light field reconstruction, a crucial domain in computer vision, generates 3D models of real-world scenes by combining multi-view images. Multi-view stereo technology is essential in this process, leveraging images from various angles to reconstruct 3D structures. It is widely used in applications such as cultural heritage preservation, robotics, and virtual reality. However, current methods often depend on rigid feature extraction and fusion techniques, restricting their capacity to fully exploit the rich multi-view data. This frequently results in blurred reconstructions, particularly in complex scenes with intricate edges and contours. To address these limitations, we propose CALFNet, a novel light field reconstruction method based on a channel attention mechanism. CALFNet combines this attention mechanism with a detail loss module, enabling the network to prioritize crucial channel information and edge features. This significantly enhances the accuracy and completeness of 3D reconstructions from multi-view images. Experiments on the DTU 3D reconstruction dataset demonstrate that CALFNet improves overall accuracy by 2.4% compared to existing methods. Additionally, tests on the Tanks and Temples dataset demonstrate the model's strong generalization capabilities, effectively handling diverse reconstruction tasks across various scenarios. This advancement paves the way for new applications in fields such as cultural heritage, robotics, and virtual reality. © 2025 SPIE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

RepF-Net: Distortion-Aware Re-projection Fusion Network for Object Detection in Panorama image 16th

RepF-Net: Distortion-Aware Re-projection Fusion Network for ...

引用

16th Asian conference on Computer Vision (ACCV)

作者： Li, Mengfan Meng, Ming Zhou, Zhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Commun Univ China Sch Data Sci & Media Intelligence Beijing Peoples R China

ISBN: (纸本)9783031263125;9783031263132

Panorama image has a large 360 degrees field of view, providing rich contextual information for object detection, widely used in virtual reality, augmented reality, scene understanding, etc. However, existing methods for object detection on panorama image still have some problems. When 360 degrees content is converted to the projection plane, the geometric distortion brought by the projection model makes the neural network can not extract features efficiently, the objects at the boundary of the projection image are also incomplete. To solve these problems, in this paper, we propose a novel two-stage detection network, RepF-Net, comprehensively utilizing multiple distortion-aware convolution modules to deal with geometric distortion while performing effective features extraction, and using the non-maximum fusion algorithm to fuse the content of the detected object in the post-processing stage. Our proposed unified distortion-aware convolution modules can be used to deal with distortions from geometric transforms and projection models, and be used to solve the geometric distortion caused by equirectangular projection and stereographic projection in our network. Our proposed non-maximum fusion algorithm fuses the content of detected objects to deal with incomplete object content separated by the projection boundary. Experimental results show that our RepF-Net outperforms previous state-of-the-art methods by 6% on mAP. Based on RepF-Net, we present an implementation of 3D object detection and scene layout reconstruction application.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

NViST: In the Wild New View Synthesis from a Single image with Transformers

NViST: In the Wild New View Synthesis from a Single Image wi...

引用

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

作者： Jang, Wonbong Agapito, Lourdes UCL Dept Comp Sci London England

ISBN: (纸本)9798350353006

We propose NViST, a transformer-based model for efficient and generalizable novel-view synthesis from a single image for real-world scenes. In contrast to many methods that are trained on synthetic data, object-centred scenarios, or in a category-specific manner, NViST is trained on MVImgNet, a large-scale dataset of casually-captured real-world videos of hundreds of object categories with diverse backgrounds. NViST transforms image inputs directly into a radiance field, conditioned on camera parameters via adaptive layer normalisation. In practice, NViST exploits fine-tuned masked autoencoder (MAE) features and translates them to 3D output tokens via cross-attention, while addressing occlusions with self-attention. To move away from object-centred datasets and enable full scene synthesis, NViST adopts a 6-DOF camera pose model and only requires relative pose, dropping the need for canonicalization of the training data, which removes a substantial barrier to it being used on casually captured datasets. We show results on unseen objects and categories from MVImgNet and even generalization to casual phone captures. We conduct qualitative and quantitative evaluations on MVImgNet and ShapeNet to show that our model represents a step forward towards enabling true in-the-wild generalizable novel-view synthesis from a single image. Project webpage: https://***/nvist_webpage.

关键词： Generalizing NeRF Novel View Synthesis Single View reconstruction Transformers

来源：评论

学校读者我要写书评

暂无评论

SCINeRF: Neural Radiance Fields from a Snapshot Compressive image

SCINeRF: Neural Radiance Fields from a Snapshot Compressive ...

引用

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

作者： Li, Yunhao Wang, Xiaodong Wang, Ping Yuan, Xin Liu, Peidong Zhejiang Univ Hangzhou Peoples R China Westlake Univ Hangzhou Peoples R China

ISBN: (纸本)9798350353006

In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high- dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, which not only reduces storage requirements but also offers potential privacy protection. Inspired by this, to take one step further, our approach builds upon the powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view image synthesis. Moreover, our method also exhibits the ability to restore high frame-rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://***/WU-CVGL/SCINeRF.

关键词： 3D from Single image Computational Imagimg

来源：评论

学校读者我要写书评

暂无评论

Two-stage Deep Learning Method for Sparse-view Computed Tomography image reconstruction 6

Two-stage Deep Learning Method for Sparse-view Computed Tomo...

引用

6th International conference on Natural Language Processing (ICNLP)

作者： Cui, Jiapei Zhang, Xuanxuan Jia, Yunfei Ye, Chenhui Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Peoples R China

ISBN: (纸本)9798350349122;9798350349115

Sparse-view computed tomography (CT) is an effective method for reducing radiation dose. The images reconstructed from insufficient data obtained from sparse-view CT suffer from severe star shaped artifacts. Therefore, reducing radiation dose will reduce imaging quality. Although deep learning methods used for sparse-view CT reconstruction have achieved impressive success, such as convolutional neural network (CNN), the reconstruction results are still too smooth, i.e. losing many details. In this work, we propose a two-stage deep learning method to reduce the artifacts of sparse-view CT images. We conducted several numerical simulation experiments to test the performance of the proposed network. The results indicate that the denoising method can significantly reduce the artifacts caused by sparse sampling and carry more detailed information than CNN.

关键词： Computed tomography sparse-view reconstruction two-stage network

来源：评论

学校读者我要写书评

暂无评论

ZeroFake: Zero-Shot Detection of Fake images Generated and Edited by Text-to-image Generation Models 24

ZeroFake: Zero-Shot Detection of Fake Images Generated and E...

引用

31st conference on Computer and Communications Security

作者： Sha, Zeyang Tan, Yicong Li, Mingjie Backes, Michael Zhang, Yang CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9798400706363

The text-to-image generation model has attracted significant interest from both academic and industrial communities. These models can generate the images based on the given prompt descriptions. Their potent capabilities, while beneficial, also present risks. Previous efforts relied on the approach of training binary classifiers to detect the generated fake images, which is inefficient, lacking in generalizability, and non-robust. In this paper, we propose the novel zero-shot detection method, called ZeroFake, to distinguish fake images apart from real ones by utilizing a perturbation-based DDIM inversion technique. ZeroFake is inspired by the findings that fake images are more robust than real images during the process of DDIM inversion and reconstruction. Specifically, for a given image, ZeroFake first generates noise with DDIM inversion guided by adversary prompts. Then, ZeroFake reconstructs the image from the generated noise. Subsequently, it compares the reconstructed image with the original image to determine whether it is fake or real. By exploiting the differential response of fake and real images to the adversary prompts during the inversion and reconstruction process, our model offers a more robust and efficient method to detect fake images without the extensive data and training costs. Extensive results demonstrate that the proposed ZeroFake can achieve great performance in fake image detection, fake artwork detection, and fake edited image detection. We further illustrate the robustness of the proposed ZeroFake by showcasing its resilience against potential adversary attacks. We hope that our solution can better assist the community in achieving the arrival of a more efficient and fair AGI.(1)

关键词： Text-To-image Models Deepfake Detection image Editing

来源：评论

学校读者我要写书评

暂无评论

Compressive Imaging reconstruction via Conditional Diffusion Model With Augmented Measurements

Compressive Imaging Reconstruction via Conditional Diffusion...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Martinez, Emmanuel Suarez, Leon Gualdrón-Hurtado, Romario Jacome, Roman Arguello, Henry Department of Systems and Informatics Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Electrical Electronics and Telecommunications Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia

ISBN: (纸本)9798350368741

Compressive imaging (CI) consists of reconstructing images from incomplete observed data. The reconstruction process involves solving an ill-posed inverse problem which is highly dependent on the number of real measurements, with a greater number of measurements typically leading to more accurate reconstructions. Due to their ability to learn data distributions, diffusion models (DM) have emerged as promising techniques for various inverse problems. Mainly, DMs solve inverse problems by conditioning the generation process to the acquired measurements. In this work, we introduce a new approach to improve this conditioning by exploiting synthetic measurements, which come from a synthetic sensing matrix. Synthetic measurements are estimated from real data via a neural network. The combined real and synthetic measurements form an augmented set, which is input into the conditional DM to enhance reconstruction capacity. Computational experiments demonstrate that augmenting measurements with the conditional DM improves performance compared to using only real measurements. © 2025 IEEE.

关键词： compressive imaging data augmentation diffusion models preconditioning

来源：评论

学校读者我要写书评

暂无评论

Registered and Segmented Deformable Object reconstruction from a Single View Point Cloud

Registered and Segmented Deformable Object Reconstruction fr...

引用

IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Henrichi, Pit Gyenes, Balazs Scheikli, Paul Maria Neumann, Gerhard Mathis-Ullrich, Franziska FAU Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (纸本)9798350318920;9798350318937

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of deformed real world objects. This is normally done using deformable object registration, which is problem specific and complex to tune. Recent methods utilize neural occupancy functions to improve deformable object registration by registering to an object reconstruction. Going one step further, we propose a system that in addition to reconstruction learns segmentation of the reconstructed object. As the resulting output already contains the information about the segments, we can skip the registration process. Tested on a variety of deformable objects in simulation and the real world, we demonstrate that our method learns to robustly find these segments. We also introduce a simple sampling algorithm to generate better training data for occupancy learning.

关键词： 3D computer vision Algorithms 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

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 << < 17 18 19 20 21 22 23 24 25 26 > >>

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：