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A Novel Nonlinear Parameter Estimation Method of Soft Tissues

Genomics, Proteomics & Bioinformatics 2017年第6期15卷 371-380页

作者： Qianqian Tong Zhiyong Yuan Mianlun Zheng Xiangyun Liao Weixu Zhu Guian Zhang School of Computer Wuhan University Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values, To provide highly precise data for estimating nonlinear param- eters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine （WCFM_SVM）. Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young＇s modulus and Poisson＇s ratio to avoid solving compli- cated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt （LM） algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.

关键词： Nonlinear parameter estimation Finite element method Substitution parameters Force correction Self-adapting Levenberg-Marquardt algorithm

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Flow-aware synthesis: A generic motion model for video frame interpolation

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Computational Visual Media 2021年第3期7卷 393-405页

作者： Jinbo Xing Wenbo Hu Yuechen Zhang Tien-Tsin Wong Department of Computer Science and Engineering the Chinese University of Hong KongHong Kong SARChina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina

A popular and challenging task in video research,frame interpolation aims to increase the frame rate of *** existing methods employ a fixed motion model,e.g.,linear,quadratic,or cubic,to estimate the intermediate warping ***,such fixed motion models cannot well represent the complicated non-linear motions in the real world or rendered ***,we present an adaptive flow prediction module to better approximate the complex motions in ***,interpolating just one intermediate frame between consecutive input frames may be insufficient for complicated non-linear *** enable multi-frame interpolation,we introduce the time as a control variable when interpolating frames between original ones in our generic adaptive flow prediction *** and quantitative experimental results show that our method can produce high-quality results and outperforms the existing stateof-the-art methods on popular public datasets.

关键词： flow-aware generic motion model video frame interpolation

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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Mixed reality based respiratory liver tumor puncture navigation

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Computational Visual Media 2019年第4期5卷 363-374页

作者： Ruotong Li Weixin Si Xiangyun Liao Qiong Wang Reinhard Klein Pheng-Ann Heng Institute of Computer Science II University of Bonn53115 BonnGermany Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina Department of Computer Science and Engineering Chinese University of Hong KongHong KongChina

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical *** with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking *** addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver *** proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.

关键词： mixed reality human computer interaction statistical motion model

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Orientation-Aware Text Proposals Network for Scene Text Detection 12th

Orientation-Aware Text Proposals Network for Scene Text Dete...

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12th Chinese Conference on Biometric Recognition, CCBR 2017

作者： Huang, Huijuan Tian, Zhi He, Tong Huang, Weilin Qiao, Yu Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9783319699226

In this paper, we present a novel Orientation-Aware Text Proposals Network (OA-TPN) for detecting text in the wild. The OA-TPN is able to accurately localize arbitrary-oriented text lines in a natural image. Instead of detecting the whole text line at one time, the OA-TPN detects sequences of small-scale orientation-aware text proposals. To handle text lines with different orientations, we utilize deep networks to jointly estimate text proposals with associate directions at the convolutional maps. Final text bounding boxes can be generated from the predicted text proposals by implementing a proposed text-line construction approach. The proposed text detector works reliably on multi-scale and multi-orientation text with single-scale images. Experimental results on the MSRA-TD500 and SWT demonstrate the effectiveness of our methods. © 2017, Springer International Publishing AG.

关键词： Convolution

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Spherical blue noise 26

Spherical blue noise

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26th Pacific Conference on Computer Graphics and Applications, Pacific Graphics 2018

作者： Wong, Kin-Ming Wong, Tien-Tsin The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9783038680734

We present a physically based method which generates unstructured uniform point set directly on the S2-sphere. Spherical uniform point sets are useful for illumination sampling in Quasi Monte Carlo (QMC) rendering but it is challenging to generate high quality uniform point sets directly. Most methods rely on mapping the low discrepancy unit square point sets to the spherical domain. However, these transformed point sets often exhibit sub-optimal uniformity due to the inability of preserving the low discrepancy properties. Our method is designed specifically for direct generation of uniform point sets in the spherical domain. We name our generated result as Spherical Blue Noise point set because it shares similar point distribution characteristics with the 2D blue noise. Our point sets possess high spatial uniformity without a global structure, and we show that they deliver competitive results for illumination integration in QMC rendering, and general numerical integration on the spherical domain. © 2018 The Author(s).

关键词： Spheres

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Inception classification and object detection based joint-CNN for indoor scene classification

Inception classification and object detection based joint-CN...

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2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018

作者： Tian, Yarding Zhang, Weitong Zhang, Qieshi Lu, Gang School of Computer Science Shaanxi Normal University Xi'an China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9789881404886

While convolutional neural network (CNN) has been successfully used in many fields including single-label scene classification, it is vital to note that real world scenes generally contain multiple semantics and multi-label, especially in the indoor scene classification due to its content complexity. At the same time, most approaches try to make the network much deeper to make sure that they can extract more detail information. However, the deeper network will cause a lot of problems such as the increase of computational costs and network costs and so on. In order to solve these problems, this paper presents a novel framework which called Joint-CNN based on the proposed special label extraction and network structure. Extensive experiments on various data sets show that our method has enhanced the performance on MIT indoor67 and SUN397 data sets. © 2018 Newswood Limited. All rights reserved.

关键词： Object detection

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Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

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Green Energy & Environment 2023年第3期8卷 820-830页

作者： Dashuai Wang Runfeng Cao Shaogang Hao Chen Liang Guangyong Chen Pengfei Chen Yang Li Xiaolong Zou Shenzhen Geim Graphene Center Tsinghua-Berkeley Shenzhen Institute&Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Tencent Shenzhen518054China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong Kong999077China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen518055China

Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of ***,a unified understanding of underlying mechanism for further optimization is still *** this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as *** method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various *** ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic *** our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying ***,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of ***,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR *** work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.

关键词： Cu-based single-atom alloy CO adsorption Machine learning First principles CO_(2)reduction reaction

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Multi-resolution 2D-3D registration of vascular intervention based on Normalized mutual information and gradient difference fusion

Multi-resolution 2D-3D registration of vascular intervention...

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2022 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2022

作者： Wang, Jun Ning, Zhigang Liao, Xiangyun Wang, Qiong School of Electrical Engineering University of South China Hengyang421001 China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Shenzhen518055 China

ISBN: (纸本)9781510654846

The registration between intraoperative 2D digital subtraction angiography and preoperative 3D computed tomography angiography can improve the visual perception of doctors during vascular interventional surgery and provide three-dimensional information of blood vessels. Therefore, improving the accuracy and robustness of 2D-3D vascular registration is the key to vascular interventional surgery. In this paper, we propose a similarity measure that fuses normalized mutual information with gradient difference and adds a multi-resolution strategy to the registration framework. Experiments show that the mTRE of the proposed method is 2.1mm, and the time of each registration iteration is 174.6s. Compared with normalized mutual information and gradient difference, the proposed method has higher accuracy and faster efficiency, and achieves better results in a shorter time. © 2022 SPIE.

关键词： Blood vessels

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A robust hand detection method based on skin probability map 2017

A robust hand detection method based on skin probability map

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9th International Conference on Signal Processing Systems, ICSPS 2017

作者： Zhao, Yan-Guo Zheng, Feng Song, Zhan Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Departments of Electronic and Electrical Engineering University of Sheffield SheffieldS1 4DE United Kingdom

ISBN: (纸本)9781450353847

Existing hand detection methods are usually implemented with Haar-like and HOG descriptors, which lack of robustness to complicate background and the change of the lighting conditions in real applications. In this paper, a robust hand detection method is presented by exploring various feature representations based on skin probability map (SPM) instead of conventional grayscale image space. We show that, in the generated SPM image, hand region can be greatly highlighted and the background regions can be suppressed. And then, the Haar-like and HOG features are calculated from the skin mask and probability maps respectively, and named as skin-Enhanced features. In the sliding window searching stage, an efficient 3-level detection framework is introduced. In the experiment, an extensive dataset is established for the training of classifiers. And the hand detection results show that, the proposed method is much more robust than conventional hand feature descriptors. © 2017 Association for Computing Machinery.

关键词： Feature extraction

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