检索结果-内蒙古大学图书馆

Proceedings of the 2020 4th International Conference on Electronic Information technology and computer Engineering

作者： Pengfei Lin Yongtang Bao Wenxiang Du Yue Qi Beihang University Beihang University Qingdao Research Institute Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Beihang University Qingdao Research Institute Qingdao China State Key Laboratory of Virtual Reality and Technology Beihang University Beihang University Qingdao Research Institute Beijing China

ISBN: (纸本)9781450387811

With the development of computer vision, Structure from Motion (SFM) which recovers sparse point clouds from image sequences has achieved great success. Large-scale scenes cannot be reconstructed with a single compute node so that we introduce a divide-and-conquer framework to solve the distributed SFM problem. First, we attach great importance to the efficiency of image matching and geometric filtering, which takes up a lot of time in the traditional SFM problem. We use the GPS information of images to calculate the GPS neighborhood. The number of image matches is greatly reduced by matching each image with only valid GPS neighbors, and a robust matching relationship is obtained. Second, the calculated matching relationship is used as the initial camera graph to be divided into multiple subgraphs by the clustering algorithm, and local SFM is executed on several computing nodes to register the local cameras. Finally, all local camera poses are integrated and optimized to complete the global camera registration. Our system can solve the structure from motion problem in large-scale scenes with accuracy and efficiency.

关键词： computer Vision Structure from Motion Graph Segmentation

来源：评论

学校读者我要写书评

暂无评论

Distributed Task Offloading based on Multi-Agent Deep Reinforcement Learning

Distributed Task Offloading based on Multi-Agent Deep Reinfo...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shucheng Hu Tao Ren Jianwei Niu Zheyuan Hu Guoliang Xing State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China The Chinese University of Hong Kong HKSAR China

ISBN: (纸本)9781665406697

Recent years have witnessed the increasing popularity of mobile applications, e.g., virtual reality, unmanned driving, which are generally computation-intensive and latency-sensitive, posing a major challenge for resource-limited user equipment (UE). Mobile edge computing (MEC) has been proposed as a promising approach to alleviate the problem, by offloading mobile tasks to the edge server (ES) deployed in close proximity to UE. However, most existing task offloading algorithms are primarily based on centralized scheduling, which could suffer from the ‘curse of dimensionality’ in large MEC environments. To address this issue, this paper proposes a fully distributed task offloading approach based on multi-agent deep reinforcement learning, whose critic and actor neural networks are trained under the assistance of global and local network states, respectively. In addition, we design a model parameter aggregation mechanism, along with a normalized fine-tuned reward function, to further improve the learning efficiency of the training process. Simulation results show that our proposed approach could achieve substantial performance improvements over baseline approaches.

关键词： Training Solid modeling Processor scheduling Simulation Reinforcement learning virtual reality Scheduling

来源：评论

学校读者我要写书评

暂无评论

An Efficient Lossless Dual Images Secret Sharing Scheme Using Turtle Shell Reference Matrix

引用

International Journal of Network Security 2021年第2期23卷 195-204页

作者： Lin, Jiang Yi Chen, Yu Chang, Chin Chen Hu, Yu Chen School of Computer and Information Engineering Xiamen University of Technology China Key Laboratory of Fujian Universities for Virtual Reality and 3D Visualization China Department of Information Engineering and Computer Science Feng Chia University No. 100 Wenhua Road Xitun District Taichung40724 Taiwan School of Information Science and Engineering Fujian University of Technology China Department of Computer Science and Information Management Providence University Taiwan

This work proposes a novel reversible (2, 2) secret sharing scheme based on turtle shells (TS) with dual images. According to the characteristic of the TS reference matrix in a 3x3 block, each pixel in the cover image can be used to conceal an octal digit by shifting it to the appropriate location, then two shadows with high quality are generated in the end. After the location conflict problem is solved, the hidden secret data can be extracted without any error as long as both shadows are gathered, and the cover image can be restored losslessly through the pixel orientational relationship located at two shadows. The experimental results demonstrate that the proposed scheme can achieve higher embedding capacity and maintain good image quality than some existing methods. In addition, the proposed scheme is effective against statistic attacks on pixel-value difference histograms (PDH). © 2021, International Journal of Network Security. All Rights Reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Modeling Garment Seam from a Single Image

引用

Journal of computer Science & technology 2018年第3期33卷 463-474页

作者： Chen-Xu Zhang Xiao-Wu Chen Hong-Yu Wu Bin Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

We propose an automatic garment seam modeling framework to create a garment model with the seam structure from a single image. In order to achieve this, a marked seam image database and parametric seam models have been set up. Given a real seam image, we first identify the type of the seam image based on our marked semn image database and the seam parameters are parsed automatically by our sewing thread estimation method. Second the seam initial model is generated through the pre-defined parametric seam models. A garment model with the seam structure is finally obtained based on the seam position infrmation which users have marked on the garment. Moreover. we verify the effectiveness of our method with numerous experiments.

关键词： image-based 3D modeling garment modeling garment seam

来源：评论

学校读者我要写书评

暂无评论

Real-Time UAV Path Planning for Autonomous Urban Scene Reconstruction

Real-Time UAV Path Planning for Autonomous Urban Scene Recon...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Qi Kuang Jinbo Wu Jia Pan Bin Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science the University of Hong Kong Hong Kong China

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Unmanned aerial vehicles (UAVs) are frequently used for large-scale scene mapping and reconstruction. However, in most cases, drones are operated manually, which should be more effective and intelligent. In this article, we present a method of real-time UAV path planning for autonomous urban scene reconstruction. Considering the obstacles and time costs, we utilize the top view to generate the initial path. Then we estimate the building heights and take close-up pictures that reveal building details through a SLAM framework. To predict the coverage of the scene, we propose a novel method which combines information on reconstructed point clouds and possible coverage areas. The experimental results reveal that the reconstruction quality of our method is good enough. Our method is also more time-saving than the state-of-the-arts.

关键词： Buildings Image reconstruction Three-dimensional displays Path planning Drones Cameras Layout

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

arXiv

引用

arXiv 2023年

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany ETH Zürich Switzerland The Shenzhen Campus of Sun Yat-Sen University Sun Yat-Sen University China Bigo Technology Pte. Ltd Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023, CC BY-SA.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Instance Shadow Detection

Instance Shadow Detection

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Tianyu Wang Xiaowei Hu Qiong Wang Pheng-Ann Heng Chi-Wing Fu Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Instance shadow detection is a brand new problem, aiming to find shadow instances paired with object instances. To approach it, we first prepare a new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Second, we design LISA, named after Light-guided Instance Shadow-object Association, an end-to-end framework to automatically predict the shadow and object instances, together with the shadow-object associations and light direction. Then, we pair up the predicted shadow and object instances, and match them with the predicted shadow-object associations to generate the final results. In our evaluations, we formulate a new metric named the shadow-object average precision to measure the performance of our results. Further, we conducted various experiments and demonstrate our method's applicability on light direction estimation and photo editing.

关键词： Head Feature extraction Proposals Image segmentation Image color analysis Training computer vision

来源：评论

学校读者我要写书评

暂无评论

A Distributed Computation Offloading Scheduling Framework based on Deep Reinforcement Learning

A Distributed Computation Offloading Scheduling Framework ba...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Bin Dai Tao Ren Jianwei Niu Zheyuan Hu Shucheng Hu Meikang Qiu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China Texas A&M University-Commerce TX USA

Recent years have witnessed the rapid growth of smart devices and mobile applications. However, mobile applications are typically computation-intensive and delay-sensitive, while User Devices (UDs) are usually resource-limited. Mobile Edge Computing (MEC) has been proposed as a promising paradigm to mitigate the tension, where UDs’ tasks could be executed either locally on itself or remotely on the edge server via computation offloading. Lots of efficient computation offloading scheduling approaches have been proposed, whereas most of them are based on centralized scheduling which could face troubles in large-scale MEC. To address the issue, this paper proposes a distributed scheduling framework by leveraging the idea of ‘centralized training and distributed scheduling’. Furthermore, the Actor-Critic reinforcement learning is adopted to build the framework where the Actor and Critic play the roles of distributed scheduling and centralized training, respectively. Extensive simulations are conducted and the experimental results verify the effectiveness and efficiency of the proposed framework.

关键词： Training Processor scheduling Computational modeling Simulation Reinforcement learning Mobile applications Servers

来源：评论

学校读者我要写书评

暂无评论

UnrealPerson: An adaptive Pipeline towards costless person Re-identification

arXiv

引用

arXiv 2020年

作者： Zhang, Tianyu Xie, Lingxi Wei, Longhui Zhuang, Zijie Zhang, Yongfei Li, Bo Tian, Qi Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Pengcheng Laboratory Tsinghua University China University of Science and Technology of China China Xidian University China

The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, our method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline. We will publish our data synthesis toolkit and synthesized data in https://***/FlyHighest/UnrealPerson. Copyright © 2020, The Authors. All rights reserved.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

A New Time-Aware Collaborative Filtering Intelligent Recommendation System

引用

computers, Materials & Continua 2019年第8期61卷 849-859页

作者： Weijin Jiang Jiahui Chen Yirong Jiang Yuhui Xu Yang Wang Lina Tan Guo Liang Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha410205China Institute of Big Data and Internet Innovation Hunan University of Technology and BusinessChangsha410205China School of Computer Science and Technology Wuhan University of TechnologyWuhan430073China Tonghua Normal University Tonghua134002China School of Bioinformatics University of MinnesotaTwin CitiesUSA

Aiming at the problem that the traditional collaborative filtering recommendation algorithm does not fully consider the influence of correlation between projects on recommendation accuracy,this paper introduces project attribute fuzzy matrix,measures the project relevance through fuzzy clustering method,and classifies all project ***,the weight of the project relevance is introduced in the user similarity calculation,so that the nearest neighbor search is more *** the prediction scoring section,considering the change of user interest with time,it is proposed to use the time weighting function to improve the influence of the time effect of the evaluation,so that the newer evaluation information in the system has a relatively large *** experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation quality.

关键词： Fuzzy clustering time weight attenuation function Collaborative filtering method recommendation algorithm

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：