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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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Real-time multi-resolution digital elevation map creation using the sensor model of a stereovision sensor

Real-time multi-resolution digital elevation map creation us...

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International Conference on Intelligent Transportation

作者： Szilárd Mandici Sergiu Nedevschi Image Processing and Pattern Recognition Research Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781509018901

This paper presents an approach for constructing high quality Digital Elevation Maps (DEMs) from dense stereo data. As opposed to classical DEM creation algorithms, the proposed solution uses the sensor models of the stereovision sensor to correct the raw data. The direct and inverse sensor models are found from experiments. The algorithm decomposes the 2D cells containing vertical objects or object boundaries in order to model the objects more precisely. The approach distinguishes between 2 types of cells: cells containing horizontal and vertical objects. For the vertical object cells, connected components in the histogram of heights are found while in the horizontal object cells, the most representative height is determined using a mode-seeking approach similar to mean shift. The 3D reconstructed points are assigned to the cells around it by considering the probability of correspondence based on the 2D Gaussian component of the direct sensor model. In order to eliminate the noise, a similarity measure is computed between each 3D point and cell based on the calculated DEM cell descriptor and is discarded if the similarity score is smaller than a predefined threshold. The algorithm is implemented on the GPU in order to achieve a real-time execution speed and it is evaluated extensively in comparison with other DEM creation algorithms using the latest KITTI stereo benchmark data. The obtained DEM is denser, more accurate and the object boundaries are modeled more precisely.

关键词： Three-dimensional displays Robot sensing systems image reconstruction Two dimensional displays Histograms Solid modeling Mathematical model

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Unsupervised feature selection algorithm based on sparse representation 3

Unsupervised feature selection algorithm based on sparse rep...

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2016 3rd International Conference on Systems and Informatics, ICSAI 2016

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

ISBN: (纸本)9781509055210

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. © 2016 IEEE.

关键词： Feature Selection

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Robust Face recognition via Low-rank Sparse Representation-based Classification

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International Journal of Automation and computing 2015年第6期12卷 579-587页

作者： Hai-Shun Du Qing-Pu Hu Dian-Feng Qiao Ioannis Pitas Institute of Image Processing and Pattern Recognition Henan University Department of Informatics Aristotle University of Thessaloniki

Face recognition has attracted great interest due to its importance in many real-world applications. In this paper,we present a novel low-rank sparse representation-based classification(LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition.

关键词： Face recognition image classification sparse repre

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image Representation via Sub-dictionary based Sparse Coding

Image Representation via Sub-dictionary based Sparse Coding

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International Joint Conference on Neural Networks

作者： Bingxin Xu Qian Yin Ping Guo Hongzhe Liu Beijing Key Laboratory of Information Service Engineering Beijing Union University Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.

关键词： image representation Sparse coding sparse representation-based classification Linearly

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Motion cue based pedestrian detection with two-frame-filtering

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High Technology Letters 2015年第3期21卷 328-332页

作者：吕敬钦 Zhang Miaohui Yang Jie Institute of Image Processing and Pattern Recognition Shanghai Jiaotong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China

This study proposes a motion cue based pedestrian detection method with two-trame-filtering （Tff） for video surveillance. The novel motion cue is exploited by the gray value variation between two frames. Then Tff processing filters the gradient magnitude image by the variation map. Summa- tions of the Tff gradient magnitudes in cells are applied to train a pre-deteetor to exclude most of the background regions. Histogram of Tff oriented gradient （HTffOG） feature is proposed for pedestrian detection. Experimental results show that this method is effective and suitable for real-time surveil- lance applications.

关键词： pedestrian detection two-frame-filtering （TFF） Tff magnitude vector （TffMV） Histogram of Tff oriented gradient （ HTffOG） SVM video surveillance

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Actuator Fault Detection and Isolation of Combined System Based on Space Geometry Theory 34

Actuator Fault Detection and Isolation of Combined System Ba...

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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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Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

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High Technology Letters 2015年第1期21卷 39-45页

作者：胡振涛 Fu Chunling Cao Zhiwei Li Congcong Instituteof Image Processing and Pattern Recognition Henan University School of Physics and Electronics Henan University

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering *** with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is ***,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation ***,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering *** the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

关键词： maneuvering target tracking nonlinear filtering cubature Kalman filter（CKF） interacting multiple model（IMM）

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