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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Jian, Wei Zhang, Yuqi Hu, Ke Fudan University Center for Biomedical Engineering School of Information Science and Technology Shanghai China Fudan University School of Computer Science Shanghai China Fudan University School of Information Science and Technology Shanghai China

ISBN: (纸本)9798350330755

Remote photoplethysmography (rPPG) has acquired much attention in health monitoring and emotion detection in recent years due to its convenience by non-contact approaches. Most studies have primarily concentrated on the estimation of pulse signals methods from the region of interest (ROI). Due to the unequal distribution of rPPG signals on the facial parts, accurate extraction of the ROI is a critical process for acquiring dependable pulse signals. However, many of those methods for ROI extraction encounter challenges, such as background pixels and non-critical skin pixels existing in the ROI. This paper proposes an effective framework integrating face detection and segmentation to address the remote heart rate estimation in presence of the ROI extraction issues. The experimental results demonstrate the proposed framework can efficiently extract skin pixels carrying high-quality heart rate information. As a result, it achieves a significant improvement in the performance of heart rate estimation. © 2023 IEEE.

关键词： Face recognition

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Reviewing metal supported solid oxide fuel cells for efficient electricity generation with biofuels for mobility

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Journal of Energy Chemistry 2025年第4期103卷 106-153页

作者： Fábio C.Antunes João P.J.de Oliveira Ricardo S.de Abreu Thiago Dias Bruno B.N.S.Brandão Josué M.Gonçalves Josimar Ribeiro Julian Hunt Hudson Zanin Gustavo Doubek School of Electrical and Computer Engineering University of CampinasCampinas 13083-852Brazil School of Food Engineering University of CampinasCampinas 13083-852Brazil School of Chemical Engineering University of CampinasCampinas 13083-852Brazil Mackenzie Institute for Research in Graphene and Nanotechnologies(MackGraphe) Mackenzie Presbyterian InstituteSão Paulo 01302-907Brazil Electrochemistry Research and Development Laboratory(LPDE) Department of ChemistryCenter of Exact SciencesFederal University of Espírito SantoCampus GoiabeirasVitória 29075-910Brazil Biological and Environmental Science and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia

Metal-Supported Solid Oxide Fuel Cells(MS-SOFCs)hold significant potential for driving the energy *** electrochemical devices represent the most advanced generation of Solid Oxide Fuel Cell(SOFCs)and can pave the way for mass production and wider adoption than Proton Exchange Membrane Fuel Cells(PEMFCs)due to their fuel flexibility,higher power density and the absence of noble metals in the fabrication *** review examines the state-of-the-art of SOFCs and MS-SOFCs,presenting perspectives and research directions for these key technological devices,highlighting novel materials,techniques,architectures,devices,and degradation mechanisms to address current challenges and future *** such as infiltration/impregnation,ex-solution catalyst synthesis,and the use of a pre-catalytic reformer layer are discussed as their impact on efficiency and prolonged *** concepts are also described and connected with well-dispersed nano particles,hindrance of coarsening,and an increased number of Triple Phase Boundaries(TPBs).This review also describes the synergistic use of reformers with MS-SOFCs to compose solutions in energy generation from readily available ***,the End-of-Life(EoL),recycling,and life-cycle assessments(LCAs)of the Fuel Cell Hybrid Electric Vehicles(FCHEVs)were *** comparing Fuel Cell Electric Vehicles(FCEVs)equipped with(PEMFCs)and FCHEVs equipped with MS-SOFCs,both powered with hydrogen(H_(2))generated by different routes were *** review aims to provide valuable insights into these key technological devices,emphasizing the importance of robust research and development to enhance performance and lifespan while reducing costs and environmental impact.

关键词： Reform Metal-supported solid oxide fuel cell Powertrain systems Fuel cell Hybrid electric vehicle Biofuel

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DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Rich Details - Benchmark Dataset and Beyond

DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Ric...

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2023 IEEE/CVF International Conference on computer Vision, ICCV 2023

作者： Wang, Feishi Ren, Jieji Guo, Heng Ren, Mingjun Shi, Boxin Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University National Engineering Research Center of Visual Technology School of Computer Science China Peking University Ai Innovation Center School of Computer Science China Shanghai Jiao Tong University School of Mechanical Engineering China Beijing University of Posts and Telecommunications School of Artificial Intelligence China Osaka University Japan

ISBN: (纸本)9798350307184

Photometric stereo aims to recover detailed surface shapes from images captured under varying illuminations. However, existing real-world datasets primarily focus on evaluating photometric stereo for general non-Lambertian reflectances and feature bulgy shapes that have a certain height. As shape detail recovery is the key strength of photometric stereo over other 3D reconstruction techniques, and the near-planar surfaces widely exist in cultural relics and manufacturing workpieces, we present a new real-world dataset DiLiGenT-Π containing 30 near-planar scenes with rich surface details. This dataset enables us to evaluate recent photometric stereo methods specifically for their ability to estimate shape details under diverse materials and to identify open problems such as near-planar surface normal estimation from uncalibrated photometric stereo and surface detail recovery for translucent materials. To inspire future research, this dataset will open soruced at https://***/***. © 2023 IEEE.

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From Local to Global: An Anatomy-Aware Deep Learning Approach for Deformable Registration of Brain MR Images 7

From Local to Global: An Anatomy-Aware Deep Learning Approac...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Liu, Haoyu Pu, Yuanyuan Ni, Jun Wang, Linfei Wang, Xiongbin Tao, Dapeng Yang, Yanhua School Of Information Science And Engineering Yunnan University Kunming China The Second Affiliated Hospital Of Kunming Medical University Information Statistics Center Kunming China School Of Computer Science And Technology Xidian University Xi'an China

ISBN: (纸本)9798350361445

Deformable registration serves as the foundation for longitudinal and population-based medical image analysis. In cross-sectional studies of brain magnetic resonance imaging (MRI), accurately aligning the regions of interest (ROIs) of various subjects, especially in the context of specific disease analysis, poses a challenge due to the complexity of brain structures and individual variations. The existing research predominantly utilizes the convolutional neural networks (CNNs) for learning global deformable registration, with little attention paid to the independent performance of neural networks on specific anatomical tissues. To address above problem, this paper proposed an anatomy-aware deep learning approach from local optimization to global information for medical deformable registration. In the method, the information of special brain anatomical tissue structure is selected to optimize the registration accuracy for the target region, and the anatomical perception constraint is designed to capture the information of these structures, so as to ensure the physiological rationality of the final registration deformation field. Compared with the recently developed state-of-the-art approaches, the proposed registration method can achieve the highest registration accuracy on both specific anatomical tissues and the overall image texture, and maintain image smoothness. © 2024 IEEE.

关键词： Tissue

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Protein-Folding Inspired Programmable Control System for Robot Swarms

Protein-Folding Inspired Programmable Control System for Rob...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhang, Feng Gu, Yuping Wang, Zebin Yuan, Shuai Yang, Yongliang School of Electrical and Control Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang110106 China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Shenyang110168 China

ISBN: (纸本)9781665481090

In swarm robotics, most of the control algorithms are inspired by the behaviors of animal swarms. The capability gap between robots and animals, however, hinders the reliability of these algorithms. Toward developing reliable algorithms of robot swarms, a promising solution is learning from the collective behaviors of low-intelligent natural swarms, such as cell collectives and molecular assemblies. Here, we proposed a programmable control system inspired by protein folding. Using our homemade RoboFold platform, we demonstrated a protein-folding inspired programmable control method for robot swarms. By programming and designing the sequence of a thread of protein-like robots, the developed system can mimic the formation of hierarchical structures in robot swarms as the three-level of protein structures. We quantitatively characterized the specific features of self-organized morphologies. The experimental results together demonstrated the feasibility of this method. To summarize, this work successfully translated the principles of protein-folding into programming methods to control the morphology of robot swarms. Considering that the protein folding problem is essentially a physicochemical problem, this system is not only beneficial for the control of swarm robots but also has the potential to develop into a tool for studying the protein folding problem. © 2024 IEEE.

关键词： Robot programming

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A Provable Decision Rule for Out-of-Distribution Detection 41

A Provable Decision Rule for Out-of-Distribution Detection

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41st International Conference on Machine Learning, ICML 2024

作者： Ma, Xinsong Zou, Xin Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)

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FAPC: Frequency-Based Adaptive Pixel Correction for Compressed Screen Content

FAPC: Frequency-Based Adaptive Pixel Correction for Compress...

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2025 Data Compression Conference, DCC 2025

作者： Song, Zetian Zhang, Jiaqi Jia, Chuanmin Wang, Qi Chen, Lili Ma, Siwei Gao, Wen School of Computer Science Peking University National Engineering Research Center of Visual Technology China Wangxuan Institute of Computer Technology Peking University China MIGU Video Co. Ltd. China

ISBN: (纸本)9798331534714

Screen content is an important category of video. The statistical distribution of pixels in screen content exhibits substantial differences compared to camera-captured video, leading to different compression needs and challenges. Hitherto, most Screen Content Coding (SCC) tools are designed for block-based hybrid coding frameworks, which may not be suitable for emerging wavelet-based and learning-based coding frameworks. Consequently, a plug-and-play SCC tool independent of coding frameworks is lacking in the current video coding landscape. In this paper, an out-loop coding method, Frequency-based Adaptive Pixel Correction (FAPC), is proposed to improve the SCC performance for arbitrary codecs. First, a True Color Value (TCV) table is established based on the most frequently occurring pixel values. Then, the reconstructed pixels are corrected according to the TCV table. To realize precise pixel correction, an adaptive threshold derivation method is meticulously designed to control the pixel correction process. Furthermore, an inheritance coding strategy is proposed to reduce the overhead of parameter transmission. The proposed method has been integrated into three different coding frameworks. Simulation results demonstrate that the proposed method can achieve 10.66%, 3.55% and 10.67% luma component BD-BR gains on the three coding frameworks, respectively. These results prove the superiority and universality of the proposed method. © 2025 IEEE.

关键词： Image compression

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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Cloth-aware Augmentation for Cloth-generalized Person Re-identification 24

Cloth-aware Augmentation for Cloth-generalized Person Re-ide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods. © 2024 ACM.

关键词： Feature extraction

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