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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

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 Sun Yat-sen University The Shenzhen Campus of 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

ISBN: (纸本)9798350302493

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 IEEE.

关键词： Image enhancement

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Erratum to: Dynamic ocean inverse modeling based on differentiable rendering

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Computational Visual Media 2024年第3期10卷 609-609页

作者： Xueguang Xie Yang Gao Fei Hou Aimin Hao Hong Qin State Key Lab of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Qingdao Research Institute Beihang UniversityQingdao 266100Chinaand Peng Cheng LabShenzhen 518000China SKLCS Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Department of Computer Science Stony Brook University(SUNY at Stony Brook)Stony BrookNew York 11794-2424USA

Erratum to Xueguang Xie,Yang Gao,Fei Hou,Aimin Hao&Hong *** ocean inverse modeling based on differentiable *** Visual Media Vol.10,No.2,279–294,***://doi10.1007/s41095-023-0338-4 The authors apologize for a hidden error in the *** is that the images in Figs.14(a)and 14(d)were mistakenly presented as left–right mirror *** authors have flipped them to ensure that the figures now correspond correctly with others in the subfigures(b,c,e,f).The accurate version of Fig.14 is provided as below.

关键词： ocean images inverse

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The technology of Bus Network Communication for Space Environment

The Technology of Bus Network Communication for Space Enviro...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shuiqiang Pei Dongying Ma Xiaoguang Hu Hui Wang Zhengtian Li Beijing Institute of Electronic System Engineering Beijing P R China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing P R China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

The space battlefield environment is becoming increasingly complex. Multiple aircrafts often attack the same or different targets in cooperation when performing missions. The information of network interaction between aircrafts will determine the attack power of the whole system. In this paper, the SpaceWire bus with high bandwidth, high reliability, strong openness, good real-time and strong certainty is used as the inter vehicle bus, and a distributed inter aircraft bus network architecture is proposed. Through the modeling and Simulation, this paper analyzes the constraint parameters of bus network transmission under different conditions. It verifies the rationality and reliability of the design, and provides some reference for the practical application of bus network communication between aircrafts.

关键词： Analytical models Atmospheric modeling Aerospace electronics Data models Delays Aircraft Load modeling

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Haptics-mediated approaches for enhancing sustained attention: framework and challenges

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Science China(Information Sciences) 2019年第11期62卷 5-30页

作者： Dangxiao WANG Teng LI Naqash AFZAL Jicong ZHANG Yuru ZHANG State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Peng Cheng Laboratory School of Medical Sciences University of New South Wales Neuroscience Research Australia School of Biological Science and Medical Engineering Beihang University

Sustained attention is essential in the daily human activities of perception, manipulation, and locomotion. An improvement in sustained attention exhibits potential impacts in several scenarios, including the treatment of mental disorders, such as the attention-deficit/hyperactivity disorder, and the training of certain professionals, such as aircraft pilots, who work under environments with heavy cognitive loads. In this study, we review the haptics-mediated sustained attention-training approaches from the afferent and efferent perspectives based on the bidirectional information flow in the haptic channel. Subsequently, the feasibility of modulating and enhancing attention via the haptic channel is analyzed based on the studies that have investigated the correlation between attention and the afferent/efferent pathways of the haptic *** identify several research questions, including how to design diverse haptic training tasks via the afferent and/or efferent pathways and which adaptive strategies can be used to adjust the difficulty levels of haptic training tasks to ensure user engagement. Furthermore, we examine the behavioral and biological evidence that can be used to validate the training efficacy, the manner in which the neural mechanisms underlying the attention-enhancing process can be understood, and the effective variables that can be attributed to the near-and far-transfer effects. In addition, we discuss the difficulties associated with the development of novel haptic technologies. In this study, we intend to investigate the potential impact of haptic stimuli on neuroplasticity and to promote the study of haptics-mediated sustained attention training.

关键词： sustained attention attention enhancement haptic interaction robotics electroencephalogram virtual reality human-computer interaction

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Research on PCIe Bus Communication Based on NeoKylin

Research on PCIe Bus Communication Based on NeoKylin

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Linsong Gu Li Li Xiaoguang Hu Jin Xiao Lei Liu the State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Electro Mechanical Engineering Institute Beijing P R China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

This paper will discuss the research on PCIe bus communication based on NeoKylin. This paper discusses the design of the board that supports the PCIe bus communication and the development of the relevant software based on NeoKylin in detail. This paper presents the realization of PCIe bus communication based on NeoKylin. In addition, this paper gives the design scheme of the hardware circuit and the mentality of developing the relevant software. This paper can be thought as the theoretic bases and the guidance of the research on PCIe bus communication in some degree.

关键词： Industrial electronics Conferences Printed circuits Software Hardware

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Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

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SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

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Rethingking cat-eye effect target detection 49

Rethingking cat-eye effect target detection

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49th International Conference on Computers and Industrial Engineering, CIE 2019

作者： Wang, Rui Li, Li Zhang, Yan School of Electronic and Information Engineering Beihang University Beijing China State Key Lab. of Virtual Reality Technology and Systems Beihang University Beijing China

The cat-eye effect detection is of great significance in civil and military applications such as anti-sneak shots and anti-sniping. Previous cat-eye detection methods such as CEIR, MSPF, etc, basically based on the grayscale and shape of the objects in the image. Although the recognition rate of cat-eye is high, it will cause false detections that identify objects such as street lamps and glass as cat-eye targets. In recent years, deep learning has developed rapidly and has achieved the results far better than traditional methods in many directions. Therefore, we try to use deep learning methods to study in the cat-eye detection. Based on the existing detection methods, a new loss function is proposed. Experiments show that our deep learning-based detection method maintains a high recognition rate and reduces the false detection rate. The result achieves up to 0.815 AP50 and 0.319 mAP at 25FPS speed on GTX1060. © 2019, Computers and Industrial Engineering. All rights reserved.

关键词： Object detection

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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Fingertip detection and tracking based on Siamese network 49

Fingertip detection and tracking based on Siamese network

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49th International Conference on Computers and Industrial Engineering, CIE 2019

作者： Zhang, Yan Dai, Shuling Wang, Rui State Key Lab. of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronic and Information Engineering Beihang University Beijing China

Fingertip detection and tracking is an important task in human-computer interaction which has high requirements for the speed and accuracy. Due to the fingertip is small and easy to be confused with other joints of the hand, it brings great challenges to the detection and tracking. The common object detection and tracking algorithms are difficult to meet the requirements. Our work can achieve the purpose of accurately detecting and tracking the fingertip of the index finger in the video, and has good real-time performance. In our work, we first detect and track the hand, then we add a segmentation module to the work. The fingertip can be extracted from the mask of hand. We use SSD to detect the hand in the first frame of the video, as the initialization of the tracking. Then we use SiamMask to track and get the mask of the hand. Finally, we detect and track the fingertip of the index finger in the segmentation. Our method has been tested on our own dataset and has achieved good accuracy and real-time performance. The AP has reached 98% at OKS(0.5) and the speed has reached 30fps. © 2019, Computers and Industrial Engineering. All rights reserved.

关键词： Human computer interaction

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Real-time suturing simulation for virtual reality medical training

Real-time suturing simulation for virtual reality medical tr...

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作者： Yu, Peng Pan, Junjun Qin, Hong Hao, Aimin Wang, Haipeng State Key Laboratory of Virtual Reality Technology & Systems Beihang University Beijing China Peng Cheng Lab Shenzhen China Faculty of Media and Communication Bournemouth University Dorset United Kingdom Department of Computer Science State University of New York at Stony Brook New YorkNY United States Beijing Aerospace General Hospital Beijing China

At present, virtual reality (VR) -based medical simulators provide an efficient and cost-effective alternative without exposing risk to the traditional training approaches. As an essential and indispensable task in fundamental surgical skills training, the research of suturing simulation still remains insufficient in the field of virtual surgery. In this paper, we present a real-time suturing simulation framework which can handle the complex interactions between surgical instruments and soft tissue. The simulation consists of two stages: external interaction and internal coupling. External interaction involves the interplay between needle/suture and the soft tissue, which are both deformed by position-based dynamics (PBD) with different constraints. At the internal coupling stage, once the force exceeds a threshold, the needle tip will puncture and penetrate into the soft tissue and generate a path. To guarantee the needle/suture accurately following the path inside the soft tissue, we propose a novel coupling method by matching and generating the constraints among needle, suture, and penetration path. We have applied this suturing simulation into a VR laparoscopic surgery simulator with haptic force. Our experimental results demonstrate that our approach can achieve real-time performance with a high degree of visual realism and haptic fidelity. © 2020 John Wiley & Sons, Ltd.

关键词： Needles

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