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Nuclear norm regularized coding with local position-patch and nonlocal similarity for face hallucination

Digital Signal processing 2017年 64卷 107-120页

作者： Guangwei Gao Jian Yang Zhihui Lai Pu Huang Jielin Jiang Hao Gao Dong Yue Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 PR China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 PR China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 PR China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 PR China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 PR China

Learning-based face hallucination methods have received much attention and progress in past few decades. Specially, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based ones. As opposed to the existing patch based methods, where the input image patch matrix is converted into vectors before combination coefficients calculation, in this paper, we propose to directly use the image matrix based regression model for combination coefficients computation to preserve the essential structural information of the input patch matrix. For each input low-resolution (LR) patch matrix, its combination coefficients over the training image patch matrices at the same position can be computed. Then the corresponding high-resolution (HR) patch matrix can be obtained with the LR training patches replaced by the corresponding HR ones. The nonlocal self-similarities are finally utilized to further improve the hallucination performance. Various experimental results on standard face databases indicate that our proposed method outperforms some state-of-the-art algorithms in terms of both visual quantity and objective metrics.

关键词： Face hallucination Position-patch Matrix based regression Nuclear norm Nonlocal similarity

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A Full-Process, Fine-Grained, and Quantitative Rehabilitation Assessment Platform Enabled by On-Skin Sensors and Multi-Task Gait Transformer Model

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ADVANCED MATERIALS 2024年第46期36卷 e2408478页

作者： Wang, Zhixin He, Xinrun Bu, Tianzhao Pang, Bo Guo, Wei Tu, Zhongyi Zhang, Zhiqiang Xiao, Xiling Yin, Zhouping Huang, Jian Wu, Hao Huazhong Univ Sci & Technol Flexible Elect Res Ctr Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Dept Rehabil Med Wuhan 430022 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT England Huazhong Univ Sci & Technol Sch Integrated Circuits Wuhan 430074 Peoples R China

Rehabilitation of patients with lower limb movement disorders is a gradual process, which requires full-process assessments to guide the implementation of rehabilitation plans. However, the current methods can only complete the assessment in one stage and lack objective and quantitative assessment strategies. Here, a full-process, fine-grained, and quantitative rehabilitation assessments platform (RAP) supported by on-skin sensors and a multi-task gait transformer (MG-former) model for patients with lower limb movement disorders is developed. The signal quality and sensitivity of on-skin sensor is improved by the synthesis of high-performance triboelectric material and structure design. The MG-former model can simultaneously perform multiple tasks including binary classification, multiclassification, and regression, corresponding to assessment of fall risk, walking ability, and rehabilitation progress, covering the whole rehabilitation cycle. The RAP can assess the walking ability of 23 hemiplegic patients, which has highly consistent results with the scores by the experienced physician. Furthermore, the MG-former model outputs fine-grained assessment results when performing regression task to track slight progress of patients that cannot be captured by conventional scales, facilitating adjustment of rehabilitation plans. This work provides an objective and quantitative platform, which is instructive for physicians and patients to implement effective strategy throughout the whole rehabilitation process. This work develops a full-process, fine-grained, and quantitative rehabilitation assessments platform (RAP) supported by on-skin sensors and a multi-task gait transformer model for patients with lower limb movement disorders. The RAP can perform multiple assessments including fall risk, walking ability, and rehabilitation progress, covering the whole process of rehabilitation and realizing objective and quantitative rehabilitation assessment. image

关键词： full-process rehabilitation assessments lower limb movement disorders MG-former model on-skin sensors

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Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data control

Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

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IEEE Conference on Decision and control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems

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Metric Learning Based Rolling Bearing Faults Diagnosis with Curvelet Transform

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Journal of Physics: Conference Series 2019年第1期1229卷

作者： Ziming Lu Jiang-Wen Xiao Zhengyi Huang School of Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education China

Rolling bearing faults are among the primary causes of breakdown in mechanical equipment. Aiming at the vibration signals of rolling bearing which are non-stationary and easy to be disturbed by noise, a novel fault diagnosis method based on curvelet transform and metric learning is proposed. This method consists of 3 parts. The first one is feature engineering which includes reshaping the original timing features of rolling bearings, employing curvelet transform to transform reshaped features and making its coefficients as the new features. Curvelet transform can analyse the original signal from many angles. The second one is employing metric learning to map these new features into special embedding space. The last one is applying KNN classifier to detect the rolling bearing faults. Metric learning can effectively improve the performance of KNN by learning a mapping matrix to modify the distribution of samples. The proposed method overcomes the problems such as the subjectivity and blindness of manual feature extraction, poor coupling in each stage and sensitive to the effect of noise. Extensive simulations based on several data-sets show that the our method has better performance on bearing fault diagnosis than traditional methods.

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The first MICCAI challenge on PET tumor segmentation

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MEDICAL image ANALYSIS 2018年 44卷 177-195页

作者： Hatt, Mathieu Laurent, Baptiste Ouahabi, Anouar Fayad, Hadi Tan, Shan Li, Laquan Lu, Wei Jaouen, Vincent Tauber, Clovis Czakon, Jakub Drapejkowski, Filip Dyrka, Witold Camarasu-Pop, Sorina Cervenansky, Frederic Girard, Pascal Glatard, Tristan Kain, Michael Yao, Yao Barillot, Christian Kirov, Assen Visvikis, Dimitris UBL UBO IBSAM LaTIMUMR 1101INSERM Brest France Huazhong Univ Sci & Technol Sch Automat Key Lab Image Proc & Intelligent Control Minist Educ China Wuhan 430074 Hubei Peoples R China Mem Sloan Kettering Canc Ctr 1275 York Ave New York NY 10021 USA Univ Tours INSERM UMR 930 Imaging & Brain Tours France Stermedia Sp Zoo Ul A Ostrowskiego 13 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Fundamental Problems Technol Dept Biomed Engn Wroclaw Poland Univ Lyon 1 INSA Lyon INSERM UMR 1044 CREATISCNRS UMR5220 Lyon France Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Univ Rennes 1 INRIA Visages Project Team CNRSIRISA 6074INSERMVisagesUMR 1228 F-35042 Rennes France

Introduction: Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical image Computing and Computer Assisted Intervention) challenge. Materials and methods: Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value. Results: Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (Flab) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods. Conclusion: The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted b

关键词： PET functional volumes image segmentation MICCAI challenge Comparative study

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Superpixel-Based Temporally Aligned Representation for Video-Based Person Re-Identification

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SENSORS 2019年第18期19卷 3861-3861页

作者： Gao, Changxin Wang, Jin Liu, Leyuan Yu, Jin-Gang Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan 430079 Hubei Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

Most existing person re-identification methods focus on matching still person images across non-overlapping camera views. Despite their excellent performance in some circumstances, these methods still suffer from occlusion and the changes of pose, viewpoint or lighting. Video-based re-id is a natural way to overcome these problems, by exploiting space-time information from videos. One of the most challenging problems in video-based person re-identification is temporal alignment, in addition to spatial alignment. To address the problem, we propose an effective superpixel-based temporally aligned representation for video-based person re-identification, which represents a video sequence only using one walking cycle. Particularly, we first build a candidate set of walking cycles by extracting motion information at superpixel level, which is more robust than that at the pixel level. Then, from the candidate set, we propose an effective criterion to select the walking cycle most matching the intrinsic periodicity property of walking persons. Finally, we propose a temporally aligned pooling scheme to describe the video data in the selected walking cycle. In addition, to characterize the individual still images in the cycle, we propose a superpixel-based representation to improve spatial alignment. Extensive experimental results on three public datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art approaches.

关键词： person re-identification superpixel temporally aligned pooling walking cycle

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Zero-shot Text-driven Physically Interpretable Face Editing

arXiv

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arXiv 2023年

作者： Meng, Yapeng Yang, Songru Hu, Xu Zhao, Rui Li, Lincheng Shi, Zhenwei Zou, Zhengxia Image Processing Center School of Astronautics Beihang University Beijing100191 China Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory China The Netease Fuxi AI Lab Hangzhou China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods that manipulate the latent space of GANs, or diffusion-based methods that model image manipulation as a reverse diffusion process, we regard the face editing process as imposing vector flow fields on face images, representing the offset of spatial coordinates and color for each image pixel. Under the above-proposed paradigm, we represent the vector flow field in two ways: 1) explicitly represent the flow vectors with rasterized tensors, and 2) implicitly parameterize the flow vectors as continuous, smooth, and resolution-agnostic neural fields, by leveraging the recent advances of implicit neural representations. The flow vectors are iteratively optimized under the guidance of the pre-trained Contrastive Language-image Pretraining (CLIP) model by maximizing the correlation between the edited image and the text prompt. We also propose a learning-based one-shot face editing framework, which is fast and adaptable to any text prompt input. Our method can also be flexibly extended to real-time video face editing. Compared with state-of-the-art text-driven face editing methods, our method can generate physically interpretable face editing results with high identity consistency and image quality. Our code will be made publicly available. © 2023, CC BY-NC-SA.

关键词： Iterative methods

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Opinion evolution in different social acquaintance networks

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CHAOS 2017年第11期27卷 113111-113111页

作者： Chen, Xi Zhang, Xiao Wu, Zhan Wang, Hongwei Wang, Guohua Li, Wei Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Educ Minist China Image Proc & Intelligent Control Key Lab Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Nontradit Secur Ctr Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430074 Hubei Peoples R China

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion ph and variation proportion pv are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve p(v) + 2p(h) = 2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This

关键词： SOCIAL policy KINSHIP INTERPERSONAL relations TOPOLOGY PUBLIC opinion

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A high-order disturbance observer based sliding mode velocity control of mobile wheeled inverted pendulum systems

A high-order disturbance observer based sliding mode velocit...

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World Congress on intelligent control and Automation (WCICA)

作者： Songhyok Ri Jian Huang Chunjing Tao Myonghyok Ri Yongsok Ri Daeho Han School of Automation Huazhong University of Science and Technology Wuhan CHINA Department of Control Science University of Sciences Pyongyang D.P.R. of Korea Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan CHINA R&D department National Research Center for Rehabilitation Technical Aids Beijing Automation school Kim Chaek University of Technology Pyongyang D.P.R. of Korea Department of Phisics University of Sciences Pyongyang D.P.R. of Korea

ISBN: (纸本)9781467384155

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Wheels Automation Velocity control Uncertainty Observers Robustness

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Exponential stability of singularly perturbed switched systems with all modes being unstable

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AUTOMATICA 2020年第0期113卷 108800-000页

作者： Yang, Wu Wang, Yan-Wu Wen, Changyun Daafouz, Jamal Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore Univ Lorraine CNRS CRAN F-54000 Nancy France

In this paper, we study the exponential stability problem for singularly perturbed switched systems(SPSSs), in which subsystems with two-time-scale property are all unstable, and both the destabilizing and stabilizing switching behaviors coexist. To estimate the state divergence during each two consecutive switching instants, the general property of a two-dimensional matrix involving singular perturbation parameter is explored. The switching sequence is properly reordered to provide an appropriate way to describe different switching behaviors. In addition, multiple composite Lyapunov functions(MCLFs) are employed to derive some stability criteria for the nonlinear SPSSs. Furthermore, by using switching-time-dependent MCLFs and dwell time method, some computable stability condition is given for the linear case. The obtained results show the relationship between the ratio of the stabilizing switching behavior and the singular perturbation parameter. Besides, the obtained results are free of ill-conditioning and stiffness problems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Singularly perturbed switched system Exponential stability Unstable mode

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