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Self-adaptive spatial image denoising model based on scale correlation and SURE-LET in the nonsubsampled contourlet transform domain

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Science China(Information Sciences) 2014年第9期57卷 72-86页

作者： LIANG MeiYu DU JunPing LIU HongGang Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

A novel self-adaptive image denoising model based on scale correlation and Stein’s unbiased risk estimate-linear expansion of thresholds(SURE-LET)in the nonsubsampled contourlet transform domain is proposed in this *** we implement the multidimensional and translation invariant decomposition for spatial images by the nonsubsampled contourlet transform,and establish the image cross-scale description *** combining the scale correlation,we make improvements for the existing SURE-LET denoising idea and establish the self-adaptive denoising *** scale correlation calculation is needed for the coefficients at different scales and sub-bands to determine whether the coefficients are retained or processed with the adaptive SURE-LET threshold *** meanwhile a new local context self-adaptive threshold strategy is proposed in the process of scale correlation *** results both on spatial images and standard images demonstrate that the proposed algorithm performs significantly better in terms of both the visual subjective evaluation and the quantitative objective *** method can achieve better noise suppression,and effectively retain image edge details.

关键词： spatial images image denoising nonsubsampled contourlet transform SURE-LET scale correlation

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3D face reconstruction based on canonical correlation analysis and morphable model

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Journal of Computational Information Systems 2014年第6期10卷 2405-2415页

作者： Hu, Yongli Ge, Yun Sun, Yanfeng Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Metropolitan Transportation Beijing University of Technology Beijing 100124 China

The traditional 3D face reconstruction method based on morphable model represents facial shape using the knowledge of facial samples. To get good reconstruction result, it is necessary to use enough samples, such as several hundreds of 3D faces. However, for a given facial image, the 3D face reconstruction is a sample dependency problem. Usually the similar samples will achieve good reconstruction result, while the dissimilar samples will give little benefit and even bring distortion. In this paper, we present a 3D face reconstruction method using merely the facial samples related to the given facial image. In the proposed method, the correlations between 2D facial images and 3D faces are firstly established by a training method based on the canonical correlation analysis theory. Then the highly correlated 3D samples of the given facial image are selected according to the correlation distance. Based on these selected samples, a compact 3D morphable model is constructed for 3D face reconstruction. Matching the model with the given facial image, its 3D face is finally reconstructed. The experimental results show that the proposed method has good performance compared with the traditional morphable model. Copyright © 2014 Binary Information Press.

关键词： Image reconstruction

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Exploration of Semantic-aware Approach for Contextual Suggestion Using Knowledge from The Open Web 24

Exploration of Semantic-aware Approach for Contextual Sugges...

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24th Text REtrieval Conference, TREC 2015

作者： Wang, Yuan Zhang, Yongfeng Zhang, Yi Zhang, Xintong Liu, Jie Huang, Yalou College of Computer and Control Engineering Nankai University Tianjin300071 China College of Software Nankai University Tianjin300071 China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science Tsinghua University Beijing100084 China School of Engineering University of California Santa Cruz95060 United States

This paper describes our group’s first attempt on the Contextual Suggestion Track of the Twenty-fourth Text REtrieval Conference (TREC 2015). The task aims to provide recommendations on attractions for various kinds of users under different and complex contexts. TREC provides two ways to participate in the track: one is to create a web server that can respond to contextual related queries called "Live Experiment", the other is to submit run files that have all the responses to the released requests called "Batch Experiment". For Live Experiment, due to lack of training data, our approach sticks closely to the defined relevance judgement criteria and context knowledge. We take linear interpolation to combine a variety of factors and contextual related knowledge. For Batch Experiment, we further consider domain preference under user attributes, and take existing Machine Learning based methods in principle. We show that feature engineering is a vital part for attraction suggestions. We find that the performance of suggestions to the provided user profiles and contexts has been improved using domain preference analysis. © 2015 24th Text REtrieval Conference, TREC 2015 - Proceedings. All Rights Reserved.

关键词： User profile

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Face recognition based on feature selection strategy

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Journal of Information and Computational Science 2014年第12期11卷 4203-4210页

作者： Tang, Hengliang Sun, Yanfeng Zhu, Jie Zhao, Mingru Beijing Key Laboratory of Multimedia and Intelligent Software Technology College of Transportation Engineering Beijing University of Technology Beijing China Beijing Key Laboratory of Intelligent Logistics System School of Information Beijing Wuzi University Beijing China

In order to extract the effective and discriminant face features, a useful feature selection strategy is designed for face representation. The strategy can remove the facial redundancies, extract the discriminant features, promote the calculation efficiency, and also guarantee the feasibility of the recognition framework. Based on this, the sparse representation framework is improved to collect all the face features and match the recognition task. The experiments, tested on ORL, YALE, CMU-PIE and FERET face databases, demonstrate that the proposed method is effective and robust to facial pose, expression and illumination conditions to some extent. © 2014 Binary Information Press

关键词： Face recognition

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Diffusion Kalman filter for distributed estimation with intermittent observations

Diffusion Kalman filter for distributed estimation with inte...

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American Control Conference

作者： Wenling Li Yingmin Jia Junping Du Deyuan Meng Seventh Research Division Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9781479917730

We consider the problem of distributed estimation for stochastic linear systems with intermittent observations. An optimal diffusion Kalman filter has been derived by minimizing the mean-squared estimation error for each node. Convergence of the estimation error covariance is proved under some mild assumptions and an upper bound is obtained for the estimation error covariance. A critical value for the arrival rate of observations is provided such that the estimation error covariance is bounded. The effectiveness of the proposed filter is validated via a numerical example involving tracking a moving target in a sensor network.

关键词： Estimation error arrival rate Kalman filters Covariance Sensor networks Critical value Linear system Upper bound numerical example Distribute

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Distributed Tracking of Extended Targets Using Random Matrices

Distributed Tracking of Extended Targets Using Random Matric...

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IEEE Annual Conference on Decision and Control

作者： Wenling Li Yingmin Jia Deyuan Meng Junping Du Seventh Research Division Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China

ISBN: (纸本)9781479978878

This paper studies the problem of extended target tracking using multiple sensors in a distributed manner. The approach of using a random matrix for the target extension is used. The weighted Kullback-Leibler divergence is applied to fuse sensor-based estimates via exchanging information among neighboring sensors. It is shown that closed form solutions can be derived to fuse sensor-based estimates of the target state and the extension matrix, respectively. In particular, the resulting solutions coincide with the same recursive form for the posterior density functions of the target state and the extension matrix. A numerical example is provided to verify the effectiveness of the proposed filter.

关键词： extended target distributed tracking Fuses Target tracking tracking Random

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Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

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Context-aware Estimation Approach based on Participatory Sensing

Context-aware Estimation Approach based on Participatory Sen...

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IEEE Global Communications Conference

作者： Yingying Guo Yan Sun Tin-Yu Wu Mohammad S. Obaidat Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Department of Computer Science and Information Engineering National Ilan University Department of Computer and Information Science Fordham University

ISBN: (纸本)9781479959532

In participatory sensing applications, the inaccuracy and conflict of the reported data, commonly caused by the error rate of participants or the differences of observation context, have received more and more attention. Estimating the real status of facilities according to observations becomes a big challenge for participatory sensing. Towards this end, we propose a context-aware estimation approach based on participatory sensing in this paper. We first model the effects of observation context, and then propose an iterative method to infer the error rate of participants and estimate the real status with high precision. Our method is verified using a public facilities monitoring application in our campus, and tested via extensive simulations. The results demonstrate that the proposed method outperforms recent popular three-estimates algorithm and OtO EM algorithm.

关键词： Participatory sensing Context Estimation Simulation Error analysis simulating Context awareness Public utilities iterative methods

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A PSNR-Controllable Data Hiding Algorithm Based on LSBs Substitution

A PSNR-Controllable Data Hiding Algorithm Based on LSBs Subs...

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IEEE Global Communications Conference

作者： Huaibo Sun Hong Luo Tin-Yu Wu Mohammad S. Obaidat Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Department of Computer Science and Information Engineering National Ilan University Department of Computer and Information Science Fordham University

ISBN: (纸本)9781479959532

There are more and more systems using mobile devices to perform sensing tasks, but these increase the risk of leakage of personal privacy and data. Data hiding is one of the important ways for information security. Even though many data hiding algorithms have worked on providing more hiding capacity or higher PSNR, there are few algorithms that can control PSNR effectively while ensuring hiding capacity. In this paper, with controllable PSNR based on LSBs substitution - PSNR-Controllable Data Hiding (PCDH), we first propose a novel encoding plan for data hiding. In PCDH, we use the remainder algorithm to calculate the hidden information, and hide the secret information in the last x LSBs of every pixel. Theoretical proof shows that this method can control the variation of stego image from cover image, and control PSNR by adjusting parameters in the remainder calculation. Then, we design the encoding and decoding algorithms with low computation complexity. Experimental results show that PCDH can control the PSNR in a given range while ensuring high hiding capacity. In addition, it can resist well some steganalysis. Compared to other algorithms, PCDH achieves better tradeoff among PSNR, hiding capacity, and computation complexity.

关键词： Data Hiding PSNR Steganalysis Hiding Capacity Hidden data Peak signal-to-noise ratio adjustable parameter

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A context-aware learning algorithm for mobile users' adaptive option preferences

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Journal of Computational Information Systems 2014年第15期10卷 6661-6668页

作者： Liu, Shudong Meng, Xiangwu Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China School of Computer Beijing University of Posts and Telecommunications Beijing China

Recently, many researches on context-aware computing theory gradually make mobile networking service development in direction of universality and personalization. Mining and learning users' real-time personal preferences is the premise of providing users with personalized networking service at anytime and anywhere. It is shown that users' personal preferences for networking services are not only affected by their personal interest but also by their contexts. Given all contextual information around users, according to users' average volatility of all services in each context, we firstly identify which contextual information could have a direct and material impact on users' personal preferences for networking services, which is called users' adaptive option preferences. Based on these contextual information, qualitatively construct an analytical hierarchy process model for users' adaptive option preferences, and quantitatively analyze affection weights of contextual information on users' personal preferences. Finally we propose a novel context-aware learning algorithm for mobile users' adaptive options preferences. Experimental results demonstrate that this approach has good performances on learning accuracy and time complexity. © 2014 Binary Information Press

关键词： Learning algorithms

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