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第三十四届中国控制会议

作者： CHENG Qianshuai QIAO Dianfeng HOU Yandong JIN Yong Institute of Image Processing and Pattern Recognition Henan University

ISBN: (纸本)9781467374439

In this paper, we propose an algorithm to solve the issue of actuator fault FDI of combined system. The algorithm is based on space geometry method. Firstly, we use space division theory and space projection operation of unobservability subspace to solve system matrix parameters. Then build a residual generator to realize decoupling corresponding relation of residuals and faults in combined system. Finally, the feasibility and effectiveness of the algorithm are shown for actuator FDI by simulations.

关键词： Combined System,Fault Detection and Isolation(FDI),Space Geometry Theory,Residual Generator

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Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

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High Technology Letters 2015年第3期21卷 301-308页

作者：胡振涛 Fu Chunling Li Junwei Institute of Image Processing and Pattern Recognition Henan University College of Physics and Electronics Henan University

Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is ***,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in *** the object is to further improve state estimation precision of nonlinear ***,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo *** addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of ***,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.

关键词： nonlinear filter observation iterated update ensemble Kalman filter （EnKF） probabilistic data association （PDA）

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Predicting Transmembrane β-barrel Segments with Chain Learning and Sparse Coding

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IFAC-PapersOnLine 2015年第28期48卷 245-250页

作者： Xi, Yin Hong-Bin, Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

We developed a novel method named MemBrain-TMB to predict the spanning segments of transmembrane Â-barrel from amino acid sequence. MemBrain-beta is a statistical machine learningbased model, which is constructed using a new chain learning algorithm with the input features are encoded by the image sparse representation approach. To deal with the diverse loop length problem, we applied a dynamic threshold method, which is particularly useful for enhancing the recognition of short loops and tight turns. MemBrain-TMB achieves a Q2 accuracy of 93% and SOV of 97% on the benchmark dataset, which is 5%~10% higher than other existing predictors. © 2015

关键词： Learning algorithms Forecasting image processing Learning systems Machine learning pattern recognition Amino acid sequence Benchmark datasets Dynamic threshold Evaluation Input features Prediction methods Sparse representation Transmembranes

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Brain MRI Segmentation with Patch-based CNN Approach

Brain MRI Segmentation with Patch-based CNN Approach

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第35届中国控制会议

作者： Zhipeng CUI Jie YANG Yu QIAO Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

Brain Magnetic Resonance image(MRI) plays a non-substitutive role in clinical *** symptom of many diseases corresponds to the structural variants of *** structure segmentation in brain MRI is of great importance in modern medical *** methods were developed for automatic segmenting of brain MRI but failed to achieve desired *** this paper,we proposed a new patch-based approach for automatic segmentation of brain MRI using convolutional neural network(CNN).Each brain MRI acquired from a small portion of public dataset is firstly divided into *** of these patches are then used for training CNN,which is used for automatic segmentation of brain *** results showed that our approach achieved better segmentation accuracy compared with other deep learning methods.

关键词： CNN Deep Learning Brain MRI Segmentation Patch-based

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An interactive visual user interface model for multimedia open annotation tools

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2016年第7期11卷 103-118页

作者： Xu, Zhuoming Guo, Wenping Wu, Ting Ni, Lixian College of Computer and Information Hohai University Nanjing210098 China Institute of Image Processing & Pattern Recognition Taizhou University Taizhou317000 China

The W3C Open Annotation Community Group’s Open Annotation (OA) Data Model is emerging as a standardized Web Annotation Data Model. We argue that a friendly interactive visual user interface for OA Data Model based annotation tools, which is largely based on an effective user interface model, plays an essential role in the usability of the tools. In this paper we propose an Interactive Visual User Interface Model for Open Annotations (or simply IVUIM4OA), which allows the annotating user to dynamically transform OA data into a visual representation and to interactively manipulate the visual objects to modify the OA data or produce new one. IVUIM4OA is an adaptation of a generic information visualization model, the Data State Reference Model, for the specifics of the visualization of OA data;it is further extended with view operators that enable the user to interact with the visual objects. We implemented a prototype multimedia annotation tool on the basis of IVUIM4OA and conducted case study experiments with the prototype tool. The implementation and experimental results show that the proposed IVUIM4OA is feasible and works well with the OA Data Model, in such a way that the annotating user can obtain a better understanding of and more intuitive interaction with the annotation data. © 2016 SERSC.

关键词： Human computer interaction

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

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第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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Objective resolution measurement in single particle reconstructions based on a new spectral signal-to-noise ratio estimation

Objective resolution measurement in single particle reconstr...

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第28届中国控制与决策会议

作者： Shuai Wang Biao Zhang Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

The resolution measurement of 3D reconstructed density map in single particle reconstruction is an important and still an open *** this paper,we propose a new protocol to measure the resolution just from the reconstructed density *** approach estimates spectral signal-to-noise ratio(SSNR) of 3D reconstructed map by computing the ratio of signal power to noise power in frequency *** power distributions of signal and noise are estimated from structure particle region and surrounding region segmented by applying a mask *** proposed protocol of calculating SSNR,which we term mask-SSNR(mSSNR),is independent of the reconstruction algorithms and can be used for density maps reconstructed with any reconstruction ***,the mSSNR neither needs to split the dataset into halves like the Fourier shell correlation(FSC) approach,nor any original images or intermediate data like other SSNR calculation methods in this *** mSSNR provides a direct calculation of SSNR based on its original definition,and is proven to be a better approach.

关键词： Electron microscopy Resolution measurement Spectral signal-to-noise ratio Signal processing

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Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

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High Technology Letters 2015年第2期21卷 132-139页

作者：胡振涛 Hu Yumei Li Song Institute of Image Processing and Pattern Recognition Henan University College of Computer and Information Engineering Henan University

Aiming at the adverse effect caused by limited detecting probability of sensors on filtering preci- sion of a nonlinear system state, a novel muhi-sensor federated unscented Kalman filtering algorithm is proposed. Firstly, combined with the residual detection strategy, effective observations are cor- rectly identified. Secondly, according to the missing characteristic of observations and the structural feature of unscented Kalman filter, the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given. The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix, when the phe- nomenon of observations missing occurs. Finally, based on the realization mechanism of federated filter, a new fusion framework of state estimation from each local node is designed. And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observa- tions. The theory analysis and simulation results show the feasibility and effectiveness of the pro- posed algorithm.

关键词： nonlinear estimation intermittent observations unscented Kalman filter federated filter

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Unsupervised Feature Selection Algorithm Based on Sparse Representation

Unsupervised Feature Selection Algorithm Based on Sparse Rep...

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International Conference on Systems and Informatics

作者： Guoqing Cui Jie Yang Masoumeh Zareapoor Jiechen Wang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Complex Network and Control Lab Shanghai Jiao Tong University

ISBN: (纸本)9781509055227

Feature selection, as a preprocessing step to machine learning, plays a pivotal role in removing irrelevant data, reducing dimensionality and improving performance evaluations. Recent years, sparse representation has become a useful tool for both supervised and unsupervised feature selection. So far, most of these algorithms still have many problems such as large computation load, performance with poor stability. Thus, this paper proposes a new unsupervised feature selection algorithm via sparse representation (UFSSR), with respect to efficiency and effectiveness. Firstly, this paper reconstructs part of data matrix via sparse representation, which makes the proposed algorithm be robust and independent of domain knowledge. Then, to reduce the reconstruction error, a new feature evaluation function is given to rank all features. Theoretical analysis and experiments compared with many popular algorithms on a set of datasets demonstrate the improvements brought by UFSSR.

关键词： Unsupervised feature selection Sparse representation Feature evaluation function Computation load Stability

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