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SSRN 2023年

作者： Zohaib, Mohammad Padalkar, Milind Gajanan Morerio, Pietro Taiana, Matteo Del Bue, Alessio Pattern Analysis & Computer Vision Italian Institute of Technology Genoa Italy Department of Marine Electrical Electronic and Telecommunications Engineering University of Genoa Italy

This paper presents a novel method to estimate 3D keypoints from single-view RGB images. Our network is trained in two steps using a knowledge distillation framework. In the first step, the teacher is trained to extract 3D features from point cloud data, which are used in combination with 2D features to estimate the 3D keypoints. In the second step, the teacher teaches the student module to hallucinate the 3D features from RGB images that are similar to those extracted from the point clouds. This procedure helps the network during inference to extract 2D and 3D features directly from images, without requiring point clouds as input. Moreover, the network also predicts a confidence score for every keypoint, which is used to select the valid ones from a set of N predicted keypoints. This allows the prediction of different number of keypoints depending on the object’s geometry. We use the estimated keypoints for computing the relative pose between two views of an object. The results are compared with those of KP-Net[1] and StarMap [2], which are the state-of-the-art for estimating 3D keypoints from a single-view RGB image. The average angular distance error of our approach (5.94[[EQUATION]]) is 8.46[[EQUATION]] and 55.26[[EQUATION]] lower than that of KP-Net (14.40[[EQUATION]]) and StarMap (61.20[[EQUATION]]), respectively. © 2023, The Authors. All rights reserved.

关键词： Distillation

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ISGAN: Unsupervised Domain Adaptation With Improved Symmetric GAN for Cross-Modality Multi-Organ Segmentation

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IEEE Journal of Biomedical and Health Informatics 2024年第6期29卷 3874-3885页

作者： Jiapeng Li Yifan Zhang Lisheng Xu Yudong Yao Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled target domain. The conventional UDA segmentation strategy aims to integrate image generation and segmentation. However, conventional image generation modules only consider information from a single domain (source or target), resulting in visual inconsistencies. The image generation module may also lack anatomical constraints, leading to incorrect pseudo-label generation. To address these issues, we propose an improved symmetric generative adversarial network (ISGAN). Unlike conventional approaches that perform domain adaptation only in the source or target domain, ISGAN adopts a symmetric architecture using two-path domain adaptation to reduce the visual difference. In addition, ISGAN adopts a bidirectional training strategy to optimize the image generation and segmentation modules. The bidirectional training strategy introduces the anatomical constraints into the image generation module, thereby reducing the generation of incorrect pseudo labels. Finally, we validate ISGAN on two cross-modality datasets (the MMWHS cardiac dataset and Abdomen dataset). ISGAN delivers promising segmentation and generalization performance compared with state-of-the-art UDA methods.

关键词： image segmentation Training Biomedical imaging image synthesis Magnetic resonance imaging Computed tomography Feature extraction Generative adversarial networks Generators Diffusion models

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TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud analysis

arXiv

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arXiv 2022年

作者： Melnyk, Pavlo Robinson, Andreas Felsberg, Michael Wadenbäck, Mårten Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform-an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons-and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://***/pavlo-melnyk/tetrasphere. Copyright © 2022, The Authors. All rights reserved.

关键词： Neurons

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Sparkling hot spots in perovskite solar cells under reverse bias

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ChemPhysMater 2022年第1期1卷 71-76页

作者： Weiqi Li Keqing Huang Jianhui Chang Caiqi Hu Caoyu Long Hai Zhang Xavier Maldague Biao Liu Jianqiao Meng Yuxia Duan Junliang Yang School of Physics and Electronics Central South UniversityChangsha 410083China Computer Vision and Systems Laboratory Department of Electrical and Computer EngineeringUniversitéLaval1065av.de la MédecineQuébec(QC)Canada G1V 0A6

Perovskite solar cells(PSCs)are attracting much attention and are on the way to ***,some modules are subject to reverse bias in actual fields,so it is meaningful to investigate the reverse-bias behav-ior of ***,an in-situ temperature and current measurement technique was ***,some hot spots were observed and then quickly disappeared in the reverse biased PSCs,along with the simultaneous increase in the current and local ***,the potential mechanism has been revealed and *** abnormal bulge in the perovskite film was found at the hot ***,the appearance and disappearance of hot spots were perfectly explained by band bending and tunneling current caused by ion ***-tionally,statistical analysis suggested that sparkling hot spots were related to reverse voltage and efficiencies of *** research provides a great significance for the study of PSCs under reverse bias.

关键词： Perovskite solar cells Reverse bias Hot spots Stability

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Exploring Seasonal Variability in the Context of Neural Radiance Fields for 3D Reconstruction on Satellite imagery 46th

Exploring Seasonal Variability in the Context of Neural R...

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Kåreborn, Liv Ingerstad, Erica Berg, Amanda Karlsson, Justus Haglund, Leif Maxar International Sweden AB Ebbegatan 13 Linköping582 13 Sweden AI Sweden Folkungagatan 44 Stockholm118 26 Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping581 83 Sweden

ISBN: (纸本)9783031851865

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualization, the study examines the model’s ability to capture and predict seasonal changes, highlighting specific challenges and strengths. Results showcase the impact of the sun direction on predictions, revealing nuanced details in seasonal transitions, such as snow cover, color accuracy, and texture representation in different landscapes. Given these results, we propose Planet-NeRF, an extension to Sat-NeRF capable of incorporating seasonal variability through a set of month embedding vectors. Comparative evaluations reveal that Planet-NeRF outperforms prior models in the case where seasonal changes are present. The extensive evaluation combined with the proposed method offers promising avenues for future research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 3D reconstruction

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Efficient Object Rearrangement via Multi-view Fusion

Efficient Object Rearrangement via Multi-view Fusion

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dehao Huang Chao Tang Hong Zhang Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object’s pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment. For videos, please visit https: //***/view/multi-view-rearr.

关键词： Visualization Accuracy Databases Simulation Standards organizations Pose estimation Pipelines

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CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

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Quantitative Biology 2024年第3期12卷 329-334页

作者： Jianfeng Cao Lihan Hu Guoye Guan Zelin Li Zhongying Zhao Chao Tang Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina College of Computer and Information Hohai UniversityNanjingChina Center for Quantitative Biology Peking UniversityBeijingChina Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina Peking-Tsinghua Center for Life Sciences Peking UniversityBeijingChina School of Physics Peking UniversityBeijingChina

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant *** this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during *** *** on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house *** results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a~1.5-min interval and covers *** embryogenesis from the 4-cell to 350-cell *** robustness of CShaperApp is also validated with the datasets from different ***,modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future *** cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data *** software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.

关键词： C.elegans embryogenesis cellular morphology cellular segmentation deep learning desktop software

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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Deep Learning in Palmprint Recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Pose focus transformer meet inter-part relation

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Expert Systems with Applications 2024年 240卷

作者： Luo, Yanmin Lin, Hongwei Huang, Wenlin Wang, Youjie Du, Jixiang Guo, Jing-Ming College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 China

Human pose estimation in crowded scenes is a challenging task. Due to overlap and occlusion, it is difficult to infer pose clues from individual keypoints. We proposed PFFormer, a new transformer-based approach that treats pose estimation as a hierarchical set prediction problem that first focuses on human windows and coarsely predicts whole-body poses globally within them. In PFFormer, we designed a Windows Clustering Transformer (WCT), which reorganizes the image windows by filtering the attentive windows and fusing the inattentive ones, allowing the transformer to focus on the important regions while reducing the interference from the complex background, followed by compensating for the loss of information with a global transformer. Then we partition the learned body pose into a set of structural parts and perform the Inter-Part Relation Module (IPRM) to capture the correlation between multiple parts. These full-body poses and component features are refined at a finer level through the Part-to-Joint Decoder (PJD). Extensive experiments show that PFFormer performs favorably against its counterpart on challenging datasets, including COCO2017, CrowdPose, and OChuman datasets. The performance of crowded scenes, in particular, demonstrates the robustness of the proposed methods to deal with occlusion. © 2023 Elsevier Ltd

关键词： Information filtering

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