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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Lin, Shisong Zhuang, Xiaodong Huang, Shiyun Liu, Yahui Shen, Linlin Liao, Xinxue Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Cardiology department First affiliated hospital of Sun Yat-sen University Guangzhou China NHC Key Laboratory of Assisted Circulation Sun Yat-sen University Guangzhou China Shenzhen National Medical Science and Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9781728148526

CHD (Coronary Heart Disease) is one of the leading causes of cardiovascular disease deaths. Invasive coronary arteriography is one of the widely used approaches to diagnose CHD. However, the time cost and expenses for most of the diagnosis methodologies are high and some patients are reluctant to do such a diagnosis. In this paper, we aim to develop a novel low cost method to predict CHD. As suggested by the cardiologists, there are substantial differences between the facial images of patients with CHD and those of healthy subjects. In this paper, we conduct an automatic analysis of the texture features extracted from eight ROIs (Regions of Interests) of the face images. Based on the texture features, random forest and decision tree are used to predict whether the subject has a CHD, or not. The experimental results on a set of 1528 face images collected from 309 subjects suggest that, our approach achieved a promising, i.e. 72.73%, prediction accuracy. © 2019 IEEE.

关键词： Diagnosis

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Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation

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Journal of Bionic Engineering 2020年第6期17卷 1163-1174页

作者： Ning Li Tie Yang Yang Yang Peng Yu Xiujuan Xue Xingang Zhao Guoli Song Imad HElhajj Wenxue Wang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Itelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Bejing 100049China Rehabilitation Center for the Disabled Shenyang 110015China Vision and Robotics Lab Department of Electrical and Computer EngineeringAmerican University of BeirutBeirut 11072020Lebanon Emerging Technologies Instinute Department of Industrial&Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong 999077China

Exoskeleton robots have demonstrated the potential to rehabilitate stroke ***,poor human-machine physiological coupling causes unexpected damage to human of muscles and ***,inferior humanoid kinematics control would restrict human natural *** to deal with these problems results in bottlenecks and hinders its *** this paper,the simplified muscle model and muscle-liked kinematics model were proposed,based on which a soft wrist exoskeleton was established to realize natural human ***,we simplified the redundant muscular system related to the wrist joint from ten muscles to four,so as to realize the human-robot physiological ***,according to the above human-like musculoskeletal model,the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the *** results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3%and 35.6%,***,the humanoid motion trajectories similarity of the robot reached 91.5%.Stroke patients could recover 90.3%of natural motion ability to satisfy for most daily *** work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.

关键词： stroke rehabilitation simplified muscle model soft wrist exoskeleton robot bionic kinematics model

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A New Dynamic Routing Network for Monocular Depth Estimation 12

A New Dynamic Routing Network for Monocular Depth Estimation

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Luo, Zhehao Luo, Sijin Liang, Guoyuan Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China University of Chinese Academy of Sciences Beijing China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems 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

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Depth estimation is an essential task for understanding the geometry of 3D scenes. Compared with multi-view-based methods, monocular depth estimation is more challenging for the requirement of integrating not only global information but also local cues from various parts. Recently, numerous Convolutional Neural Networks (CNNs) based approaches relying on pre-defined static architectures have been reported. Although many of them have achieved remarkable improvements in accuracy, there remains problem with the generality of the static model when it comes to diverse scenes. In this paper, a novel deep network with dynamic routing architecture is proposed with a dynamically adjustable structure with respect to different inputs, therefore leading to not only improvements on both accuracy and generality but also decrease of the model parameters. Extensive experiments of monocular depth estimation have been conducted on two well-known challenging datasets (i.e., NYUD-v2 and Make3D) as well as the mixed dataset, and have verified the effectiveness of the proposed dynamic framework. © 2022 IEEE.

关键词： Deep neural networks

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Uncertainty-Estimation With Normalized Logits For Out-of-distribution Detection

Uncertainty-Estimation With Normalized Logits For Out-of-dis...

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2023 International Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Huang, Mouxiao Qiao, Yu The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781510665040

Out-of-distribution (OOD) detection is critical for preventing deep learning models from making incorrect predictions, especially in safety-critical applications such as medical diagnosis and autonomous driving. However, neural networks often suffer from overconfidence, making high confidence predictions for OOD data that are never seen during training and may be irrelevant to training data. Determining the reliability of the prediction is still a difficult and challenging task. To address this challenge, we propose a new method called Uncertainty-Estimation with Normalized Logits (UE-NL) for robust learning and OOD detection. The method has three main benefits: (1) Neural networks with UE-NL treat every in-distribution (ID) sample equally by predicting the uncertainty score of input data and the uncertainty is added into softmax function to adjust the learning strength of easy and hard samples during training phase, making the model learn robustly and accurately. (2) UE-NL enforces a constant vector norm on the logits to decouple the effect of the increasing output’s norm from optimization process, which causes the overconfidence issue to some extent. (3) UE-NL provides a new metric, the magnitude of uncertainty score, to detect OOD data. Experiments demonstrate that UE-NL outperforms existing methods on common OOD benchmarks and is more robust to noisy ID data that may be misjudged as OOD data by other methods. © 2023 SPIE.

关键词： Forecasting

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization

Online Refinement of Low-level Feature Based Activation Map ...

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International Conference on computer vision (ICCV)

作者： Jinheng Xie Cheng Luo Xiangping Zhu Ziqi Jin Weizeng Lu Linlin Shen Shenzhen Institute of Artificial Intelligence of Robotics of Society Guangdong Key Laboratory of Intelligent Information Processing Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University

ISBN: (纸本)9781665428132

We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objects using the low-level feature based activation maps. In the first stage, an activation map generator produces activation maps based on the low-level feature maps in the classifier, such that rich contextual object information is included in an online manner. In the second stage, we employ an evaluator to evaluate the activation maps predicted by the activation map generator. Based on this, we further propose a weighted entropy loss, an attentive erasing, and an area loss to drive the activation map generator to substantially reduce the uncertainty of activations between object and background, and explore less discriminative regions. Based on the low-level object information preserved in the first stage, the second stage model gradually generates a well-separated, complete, and compact activation map of object in the image, which can be easily thresholded for accurate localization. Extensive experiments on CUB-200-2011 and ImageNet-1K datasets show that our framework surpasses previous methods by a large margin, which sets a new state-of-the-art for WSOL. Code will be available soon.

关键词： Location awareness computer vision Uncertainty Codes Drives Generators Entropy

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Robust text line detection in equipment nameplate images

Robust text line detection in equipment nameplate images

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Lai, Jiangyu Guo, Lanqing Qiao, Yu Chen, Xiaolong Zhang, Zhengfu Liu, Canping Li, Ying Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIATSenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment's. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91.2% precision and 92.6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models. © 2019 IEEE.

关键词： Nameplates

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MetaVSR: A Novel Approach to Video Super-Resolution for Arbitrary Magnification 30th

MetaVSR: A Novel Approach to Video Super-Resolution for Ar...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Hong, Zixuan Cao, Weipeng Xu, Zhiwu Chen, Zhenru Tao, Xi Ming, Zhong Cao, Chuqing Zheng, Liang College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen518107 China Anhui Province Key Laboratory of Machine Vision Inspection Yangtze River Delta HIT Robot Technology Research Institute Wuhu241000 China

ISBN: (纸本)9783031533044

Video super-resolution is a pivotal task that involves the recovery of high-resolution video frames from their low-resolution counterparts, possessing a multitude of applications in real-world scenarios. Within the domain of prevailing video super-resolution models, a majority of these models are tailored to specific magnification factors, thereby lacking a cohesive architecture capable of accommodating arbitrary magnifications. In response to this lacuna, this study introduces "MetaVSR", a novel video super-resolution model devised to handle arbitrary magnifications. This model is structured around three distinct modules: inter-frame alignment, feature extraction, and upsampling. In the inter-frame alignment module, a bidirectional propagation technique is employed to attain the alignment of adjacent frames. The feature extraction module amalgamates superficial and profound video features to enhance the model’s representational prowess. The upsampling module serves to establish a mapping correlation between the desired target resolution and the input provided in lower resolution. An array of empirical findings attests to the efficacy of the proposed MetaVSR model in addressing this challenge. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Expression-Aware Masking and Progressive Decoupling for Cross-Database Facial Expression Recognition

Expression-Aware Masking and Progressive Decoupling for Cros...

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International Conference on Automatic Face and Gesture Recognition

作者： Tao Zhong Xiaole Xian Zihan Wang Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

Cross-database facial expression recognition (CD-FER) has been widely studied due to its promising applicability in real-life situations, while the generalization performance is the main concern in this task. For improving cross-database generalization, current works frequently resort to masked auto encoder (MAE) to learn the expression representation in an unsupervised manner, and disentanglement of expression and domain features. (i) For MAE, current algorithms mainly employ random masking, and leverage the reconstruction of these masked regions to enable networks to learn the expression representation. However, these masked regions are expression-irrelevant, can not well reflect the characteristics of expression, thus are not efficient enough in representation learning. To this end, we propose an expression-aware masking in MAE to improve the learning efficiency of expression representation, by guiding MAE to mask out expression-aware regions during training. (ii) For disentanglement of expression and domain features, current algorithms realize it mainly in the deep layers. However, the coupling of these features in the shallow layers are rarely concerned, which may largely affect the disentanglement performance in deep layers. Thus, we propose a progressive decoupler to disentangle these features block by block, to use the feature disentanglement in shallow layers to facilitate that in deep layers. Extensive quantitative and qualitative results on multiple expression datasets show that our method can largely outperform the state of the arts in terms of cross-database generalization performance.

关键词： Training Representation learning Couplings Face recognition Gesture recognition Task analysis

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Polarization-controlled dual resonant lattice Kerker effects

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Nano Research 2023年第2期16卷 3195-3200页

作者： Lei Xiong Xueqian Zhao Xiang Du Shuai Chen Yuanfu Lu Hongwei Ding Guangyuan Li School of Information Science and Engineering Yunnan UniversityKunming 650500China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Electronic and InformationGuangxi UniversityNanning 530004China College of Physics and Electronic Technology Anhui Normal UniversityWuhu 241000China Tianjin H-Chip Technology Group Corporation Tianjin 300467China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen 518055China

The generalized Kerker effects have attracted increasing interests in recent years due to their abilities to manipulate the far-field properties of ***,the dual-polarized generalized Kerker effect enabling different tailoring of orthogonally-polarized electromagnetic waves has not yet been ***,we demonstrate polarization-controlled dual resonant lattice Kerker effects in periodic silicon *** varying the incident angle,the electric dipole and magnetic dipole surface lattice resonances can spectrally overlap,causing zero reflectance and unitary transmittance,i.e.,the resonant lattice Kerker *** incident angle for achieving this effect can be tuned differently for s-and p-polarizations over large regions by varying the nanodisk size or the lattice *** proposed dual-polarized resonant lattice Kerker effects open up avenues for polarization-controlled manipulation of the phase and wavefront of light with metasurfaces.

关键词： Kerker effect surface lattice resonances dual-polarized silicon metasurface

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