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arXiv 2020年

作者： Liu, Quande Dou, Qi Heng, Pheng Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong T Stone Robotics Institute Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Model generalization capacity at domain shift (e.g., various imaging protocols and scanners) is crucial for deep learning methods in real-world clinical deployment. This paper tackles the challenging problem of domain generalization, i.e., learning a model from multi-domain source data such that it can directly generalize to an unseen target domain. We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Our learning scheme roots in the gradient-based meta-learning, by explicitly simulating domain shift with virtual meta-train and meta-test during training. Importantly, considering the deficiencies encountered when applying a segmentation model to unseen domains (i.e., incomplete shape and ambiguous boundary of the prediction masks), we further introduce two complementary loss objectives to enhance the meta-optimization, by particularly encouraging the shape compactness and shape smoothness of the segmentations under simulated domain shift. We evaluate our method on prostate MRI data from six different institutions with distribution shifts acquired from public datasets. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains4 Copyright © 2020, The Authors. All rights reserved.

关键词： Urology

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

arXiv

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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

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A Novel Joint Optimization Method for Adaptive Hand-held Video Stabilization Based on Spatialtemporal Consistency

A Novel Joint Optimization Method for Adaptive Hand-held Vid...

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2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)

作者： Xiao Li Shuai Li Hong Qin Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

ISBN: (纸本)9781510885455

Video stabilization for hand-held camera is vital in many high-level video enhancement applications. Although quite a few proposed approaches have achieved remarkable success in recent years, many technical challenges still prevail for video(from hand-held camera) containing wide-range scenes and highlyvariable objects. In particular, we are lacking effective and versatile strategies to adaptively handle saliency preservation, parallax diminution, self-adaptive smoothing, cropping area decrement and video completeness in a consistent spatial-temporal fashion. To ameliorate, this paper develops a novel, joint optimization method to successively respect spatial structure consistency and temporal feature constraints. Our aim is to devise a new adaptive video stabilization technique by resorting to new modeling strategies. This paper's key originality is hinged upon joint utility of both self-adaptive intrinsic mode functions(IMFs) based on empirical mode decomposition(EMD) in temporal domain for video signal and mesh-structure constraint enforcement in the spatial domain. As a result, our new approach can optimize camera trajectory of wobbly video, and synchronously fine-tune the camera path based on key features to make the shaking video much closer to the original trend. To validate our joint optimization approach for adaptive video stabilization, we conduct comprehensive experiments on public benchmarks, and make extensive and quantitative evaluations with available state-of-the-art methods as well as popular commercial software. All of our experiments demonstrate the advantages of the joint optimization method in terms of versatility, accuracy, and efficiency.

关键词： hand-held Camera adaptive video stabilization spatial structure consistency feature-centric EMD self-adaptive IMF selection video extrapolation and interpolation

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Temporal-Spatial Consistency and Sampling Neural Network Based Adaptive Hand-held Video Deblurring

Temporal-Spatial Consistency and Sampling Neural Network Bas...

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作者： Xiao LI Shuai LI Hong QIN Ai-min HAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

Video deblurring for hand-held camera is vital in many high-level video enhancement applications. Although quite a few proposed approaches have achieved remarkable success in recent years, many technical challenges still prevail for video(from hand-held camera) containing wide-range scenes and highly-variable objects. In particular, we are lacking effective and versatile strategies to adaptively handle high-level alignment, parallax diminution, unclear area detection, sharpness enhancement in a consistent fashion. To ameliorate, this paper develops a novel method to successively respect temporal-spatial alignment and precise deblurring. Our aim is to devise a new adaptive video deblurring technique by resorting to new modeling strategies. This paper's key originality is hinged upon the joint utility of both self-adaptive intrinsic mode functions(IMFs) based on empirical mode decomposition(EMD) in the temporal domain for video signal and mesh-structure constraint enforcement in the spatial domain, beside, this paper innovatively jointly utilities the subsampling convolution, upsampling convolution and luckiness threshold value based back propagation, which takes video deblurring as a high-precision adaptive convolution. As a result, our new approach can adaptively improve the clarity of the video, and synchronously optimize the camera trajectory of wobbly video. To validate our optimization approach for adaptive video deblurring, we conduct comprehensive experiments on public benchmarks, and make extensive and quantitative evaluations with available state-of-the-art methods as well as popular commercial software. All of our experiments demonstrate the advantages of the optimization method in terms of versatility, accuracy, and efficiency.

关键词： Neural network Adaptive video deblurring Temporal alignment Spatial consistency alignment Luckiness threshold value

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Novel metaballs-driven approach with dynamic constraints for character articulation

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中国科学（信息科学） 2018年第9期61卷 250-252页

作者： Junxuan BAI Junjun PAN Yuhan YANG Hong QIN State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beihang University Qingdao Research Institute Qingdao 266100 China Department of Computer Science Stony Brook University New York 11794-4400 USA

Skinning techniques are essential for character articulation in 3D computer ***,skeleton-based methods are widely used in the animation industry for its simplicity and efficiency,especially in linear blend skinning (LBS) [1] and dual quaternion skinning (DQS) [2].However,owing to the lack of the inside volumetric representation,they suffer from joint collapse,candy-wrapper,and bulging problems.

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Prescribed-time cooperative guidance law with seeker-less followers against multiple targets

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ISA transactions 2025年

作者： Yunjie Wu Xuman An Yueyang Hua Guofei Li Bohao Li State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing 100191 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China. Electronic address: wyjmip@***. Science and Technology on Aircraft Control Laboratory Beihang University Beijing 100191 China. Electronic address: anxuman@***. China Academy of Launch Vehicle Technology Beijing 100191 China. Electronic address: by1503102@***. School of Astronautics Northwestern Polytechnical University Xi'an 710072 China. Electronic address: liguofei@***. Beijing Institute of Control and Electronic Technology Beijing 100038 China. Electronic address: libh08@***.

In this paper, a grouped prescribed-time cooperative guidance law (GPCGL) is proposed for multiple vehicles to attack multiple targets without collision, where one leader and seeker-less followers are involved in each subgroup. A distributed guidance law is presented for leaders to achieve separated time attack on their respective targets, while the guidance law for followers consists of two parts: tangential direction and normal direction in the follower-to-leader line-of-sight (LOS) frame. For the former, a proportional consensus guidance law is proposed based on an auxiliary control term to guarantee both the intra-subgroup cooperation and distance-based collision avoidance simultaneously; for the latter, a dynamic LOS angle constrained guidance law is applied to achieve direction-based collision avoidance. The proposed GPCGL is proved to be prescribed-time convergent, where the states can converge within the prescribed time. Simulations are carried out to verify the effectiveness.

关键词： Collision avoidance Grouped cooperative guidance Multiple targets Prescribed-time convergence Seeker-less followers

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Sigmoid Super-Twisting Extended state Observer and Sliding Mode Controller for Quadrotor UAV Attitude System With Unknown Disturbance

Sigmoid Super-Twisting Extended State Observer and Sliding M...

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IEEE International Conference on robotics and Biomimetics

作者： Yongfeng Rong Ran Jiao Song Kang Wusheng Chou College of Mechanical Engineering and Automation Beihang University Beijing China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

The super-twisting algorithm has been verified its effectiveness in attitude control for quadrotor with unknown disturbance. However, the discontinuous signum function in super-twisting formulation causes the unwanted chattering problem and thus affects the attitude control performance. In this paper, we replace the signum function with sigmoid function and propose the sigmoid super-twisting extended state observer (SSTESO) and sliding mode controller (SSTSMC). Besides, a rigorous convergence analysis of proposed nonlinear error system based on the Lyapunov stability theory is performed, and the parameter selection rule is given. Numerical simulations are implemented in presence of manually designed disturbance in order to verify the advantage of the proposed algorithm compared with original super-twisting algorithm. Results show that the introduction of sigmoid function can not only reduce the chattering effect, but also maintain the advantage of supertwisting algorithm.

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A Fast Method for Large-Scale Scene Data Acquisition and 3D Reconstruction

A Fast Method for Large-Scale Scene Data Acquisition and 3D ...

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IEEE International Symposium on Mixed and Augmented reality Workshops (ISMARW)

作者： Yao Li Yang Xie Xijing Wang Xun Luo Yue Qi State Key Laboratory of Virtual Reality Technology and Systems Beijing School of Computer Science and Engineering Tianjin University of Technology Tianjin Peng Cheng Laboratory Shenzhen Qingdao Research Institute of Beihang University Qingdao

ISBN: (纸本)9781728147666

Large-scale 3D scene has an important application prospect in virtual reality and augmented reality. However, due to the doubling of the amount of data in the 3D reconstruction of large-scale outdoor scene, the index of time becomes a great challenge under the condition of maintaining a certain degree of accuracy. In this paper, we provide a set of methods of aerial photography data acquisition and cluster-based 3D model reconstruction for large-scale scene. Firstly, for the data acquisition end, we adopt two strategies of track pre-planning and feedback-based trajectory planning to meet the requirements of efficient data acquisition. Secondly, we have designed and implemented a distributed 3D reconstruction system to process a large number of aerial photographs, which can quickly and robustly reconstruct large-scale 3D models. Finally, we simulate the crowd behavior based on the PEM model in the reconstructed 3D scene, which has a useful guiding significance for people's daily activities and emergency problems.

关键词： Three-dimensional displays Image reconstruction Solid modeling Computational modeling Data models Data acquisition Drones

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Dynamic Balance and Trajectory Tracking Control of Quadruped Robots Based on virtual Model Control

Dynamic Balance and Trajectory Tracking Control of Quadruped...

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第三十九届中国控制会议

作者： Guangrong Chen Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

As for a motion control framework of robots, virtual model control（VMC） can use virtual components to create virtual forces/torques. Actually, the virtual forces/torques are generated by joint actuators when the virtual components interact between robots and environments. In this paper, a virtual model control is proposed to do the dynamic balance control of quadruped robots in trot gait. In each leg, virtual model control includes swing phase control of robots and stance phase counterparts. In whole body, based on the forces/torques distribution method between two stance legs, virtual model control is mainly about the attitude control containing roll, pitch and yaw. Then, an intuitive approach of velocity control is employed for the locomotion of quadruped robots. Based on the velocity planning and control, a trajectory tracking control approach is investigated by considering four factors: terrain complexity index, curvature radius of given trajectory, distance to terminal, and maximum velocity of quadruped robots. Finally, the effectiveness of proposed controllers is validated by co-simulations.

关键词： virtual model control Dynamic balance control Trajectory tracking control Quadruped robots

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GRANet: Global refinement atrous convolutional neural network for semantic scene segmentation 25

GRANet: Global refinement atrous convolutional neural networ...

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25th IEEE International Conference on Image Processing, ICIP 2018

作者： Feng, Zhou Yong, Hu Xukun, Shen State Key Laboratory of Virtual Reality Technology and Systems Beihang University China School of New Media Art and Design Beihang University China

ISBN: (纸本)9781479970612

The main problems of complex-scene understanding and semantic scene segmentation are caused by mismatched relationships, confusion categories, and inconspicuous classes. Towards above issues, we propose a global refinement atrous convolutional neural network (GRANet) for semantic scene segmentation. To enlarge the receptive field of filters, we use atrous convolution instead of the downsampling operators. To handle the challenge caused by the existence of objects at multiple scales in a scene, we adopt multiple rates atrous convolution structure. And to overcome the problem that the current semantic segmentation architecture can not make good use of global information, we propose a multiple pooling module schemes to utilize the global context information to boost the performance of our GRANet. The proposed GRANet achieves state-of-the-art performance on the SiftFlow Dataset and attains comparable performance with other state-of-the-art works on Cityscapes dataset. © 2018 IEEE.

关键词： Semantics

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