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检索条件"机构=Key Laboratory of System Control and Signal Processing"

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Localizability estimation for mobile robots based on probabilistic grid map and its applications to localization

Localizability estimation for mobile robots based on probabi...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Zhe Liu Weidong Chen Yong Wang Jingchuan Wang State Key Laboratory of Robotics and System (HIT) Harbin China Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

A novel approach to estimate localizability for mobile robots is presented based on probabilistic grid map (PGM). Firstly, a static localizability matrix is proposed for off-line estimation over the priori PGM. Then a dynamic localizability matrix is proposed to deal with unexpected dynamic changes. These matrices describe both localizability index and localizability direction quantitatively. The validity of the proposed method is demonstrated by experiments in different typical environments. Furthermore, two typical localization-related applications, including active global localization and pose tracking, are presented for illustrating the effectiveness of the proposed localizability estimation method.

关键词： Covariance matrix Estimation Heuristic algorithms Iterative closest point algorithm Mobile robots Indexes

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Person re-identification across multi-camera system based on local descriptors

Person re-identification across multi-camera system based on...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Qiao Huang Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Tracking the same person across multiple cameras is an important task in multi-camera systems. It is also desirable to re-identify the individuals who have been previously seen with a single-camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from video surveillance system. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient Bag of Features approach for image presentation. In this way, robustness against low resolution, occlusion and pose, viewpoint and illumination changes is achieved in a very fast way. We get promising results from the evaluation with situations where a number of individuals vary continuously from a multi-camera system.

关键词： Vocabulary Cameras Histograms Feature extraction Image color analysis Support vector machine classification Kernel

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Image change detection based on cross-correlation coefficient by using Genetic Algorithm

Image change detection based on cross-correlation coefficien...

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International Conference on Agro-Geoinformatics

作者： Yunchen Pu Qiongcheng Xu Wei Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

In this paper, a novel unsupervised change detection approach based on cross-correlation coefficient is proposed. The cross-correlation coefficient is a measure of the similarity between two variables. The change detection problem can be understood as the process to partition two input images into two distinct regions, namely “changed” and “unchanged”, according to the binary change detection mask. Each region in the pair of the images of the corresponding position is considered as two sets of variables, whose cross-correlation coefficient is calculated in order to provide an optimal partition of the changed and unchanged regions. In the optimal partition, it is obvious that the cross-correlation coefficient of the set of the unchanged variables should be the maximum, while the absolute-value of that of the changed variables should be the minimum, because the corresponding unchanged regions are similar while the changed regions are quite different. Genetic Algorithm is used to obtain the optimal non-dominated solution as the change detection using cross-correlation coefficient is a multi-objective optimization problem. The simulation experiment shows that the result using the new method is effective and robust to radiometric difference.

关键词： Genetic algorithms Optimization Radiometry Remote sensing Sociology Statistics Robustness

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Background extraction algorithm based on K-means clustering algorithm and histogram analysis

Background extraction algorithm based on K-means clustering ...

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IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Yan Zhang Linkai Luo Department of Automation Xiamen University Xiamen China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

Background extraction is a fundamental task in many computer vision applications. This paper presents a novel method based on K-means Clustering Algorithm. First, the intensity values are divided into several groups by clustering method automatically. Then the mean of intensities in the most frequent group is assigned as the background intensity. The experiment results show that the proposed approach achieves more accurate background than traditional method.

关键词： Histograms Clustering algorithms Algorithm design and analysis Clustering methods Robustness Image color analysis Conferences

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Urban traffic flow prediction based on road network model

Urban traffic flow prediction based on road network model

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IEEE International Conference on Networking, Sensing and control

作者： Yanyan Xu Qing-Jie Kong Shu Lin Yuncai Liu Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

This paper addresses an issue of short-term traffic flow prediction in urban traffic networks with traffic signals in intersections. An effective spatial prediction approach is proposed based on a macroscopic urban traffic network model. In contrast with other time series based or spatio-temporal correlation methods, this research focuses on the substantial mechanism of vehicles transmission on road segments and the spatial model of the entire urban network. Furthermore, this approach employs a simple speed-density model based on the macroscopic fundamental diagram (MFD) to obtain a more accurate vehicle travel time on the link. Finally, the microscopic traffic simulation software, CORSIM, is adopted to simulate the real urban traffic, and the proposed method is used to predict the traffic flows generated by CORSIM. The simulation results illustrate that our approach performs effective prediction timely in the rush hours, as well as the suddenly changed traffic states.

关键词： Vehicles Predictive models Roads Junctions Mathematical model Turning Computational modeling

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SuperSA: Superframe design based slot allocation of Wireless Body Area Networks for healthcare systems

SuperSA: Superframe design based slot allocation of Wireless...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Haoran Li Cailian Chen Wenbin Yu Bo Yang Xinping Guan Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781467358309

Pervasive healthcare system plays an important role in emergency treatment and early detection of diseases. Wireless Body Area Network (WBAN) is the base of pervasive healthcare system. One of the major challenges of WBANs is sustainable power supply for the miniature sensor nodes. We propose a superframe based time division multiple access (TDMA) protocol for WBANs in healthcare systems. By taking the data heterogeneity into account, slot allocation scheme is proposed for the sake of sensor energy efficiency. In addition, sleep mode without extra information interaction is presented. Sensor node can turn to sleep (turn off RF but sample as usual) in the whole superframe only by judging local information. Within the system, we formulate an energy consumption optimization problem to determine the parameters. Finally, we introduce some performance metrics of system, and the simulation results show the advantages of our scheme.

关键词： Superframe Design Based

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A energy-aware clustering algorithm via game theory for wireless sensor networks

A energy-aware clustering algorithm via game theory for wire...

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International Conference on control, Automation and systems ( ICCAS)

作者： Yiping Yang Chuan Lai Lin Wang Xiaofan Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai JiaoTong University Shanghai China

Multihop communication mechanism has been widely employed in wireless sensor networks (WSNs) for its practicability and high energy efficiency. However, hot spots emerge as locations, in which nodes die quickly because of heavy relay load, leading to disruption in network service. Balancing energy consumption of nodes so as to mitigate the hot spot issue in the network is very important for prolonging network lifetime. In this paper, we propose a distributed clustering algorithm, namely Game Theoretic Clustering (GTC), which can approach to the equilibrium of the energy consumption for the wireless network. Especially, the cluster size is determined adaptively based on the game theory and the cooperation between cluster heads. Simulation results show that GTC can balance the energy consumption levels and consequently extend the network lifetime.

关键词： Wireless sensor networks Sensors Energy consumption Clustering algorithms Games Wireless communication Data models

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Uncalibrated fixed-camera visual servoing of robot manipulators by considering the motor dynamics

Uncalibrated fixed-camera visual servoing of robot manipulat...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Xinwu Liang Hesheng Wang Weidong Chen Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiaotong University Shanghai China

In this paper, the uncalibrated visual servoing problem of robot manipulators with motor dynamics will be addressed for the fixed-camera configuration. A new adaptive image-space visual servoing strategy is presented to handle uncertainties in the camera intrinsic and extrinsic parameters, robot kinematic and dynamic parameters, and motor dynamic parameters. To deal with the nonlinear dependence of image Jacobian matrix on the unknown parameters, the proposed scheme is developed based on the concept of depth-independent interaction matrix. In this way, the camera parameters and the robot kinematic parameters in the closed-loop dynamics can be linearly parameterized such that adaptive laws can be designed to estimate them on-line. Adaptive algorithms are also developed to provide estimation of unknown robot dynamic and motor dynamic parameters. Stability analysis will be performed to show asymptotic convergence of image errors using Lyapunov theory based on both rigid-link robot dynamics and full motor dynamics. Simulation results based on a two-link planar robot manipulators will be given to illustrate the performance of the proposed scheme.

关键词： Cameras Manipulator dynamics Robot kinematics Visual servoing

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LEARNING GEODESIC CRF MODEL FOR IMAGE SEGMENTATION

LEARNING GEODESIC CRF MODEL FOR IMAGE SEGMENTATION

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IEEE International Conference on Image processing

作者： Lei Zhou Yu Qiao Jie Yang Xiangjian He The Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiaotong University Shanghai China University of Technology Sydney Australia

Graph cut based on color model is sensitive to statistical information of images. Integrating priority information into graph cut approach, such as the geodesic distance information, may overcome the well-known drawback of bias towards shorter paths that occurred frequently with graph cut methods. In this paper, a conditional random field (CRF) model is formulated to combine color model and geodesic distance information into a graph cut optimization framework. A discriminative model is used to capture more comprehensive statistical information for geodesic distance. A simple and efficient parameter learning scheme based on feature fusion is proposed for CRF model construction. The method is evaluated by applying it to segmentation of natural images, medical images and low contrast images. The experimental results show that the geodesic information obtained by learning can provide more reliable object features. The dynamic parameter learning scheme is able to select best cues from geodesic map and color model for image segmentation.

关键词： Image segmentation Image color analysis Vectors Computational modeling Biomedical imaging Reliability Shape

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An Overview of Hierarchical Temporal Memory: A New Neocortex Algorithm

An Overview of Hierarchical Temporal Memory: A New Neocortex...

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2012 International Conference on Modelling,Identification and control(建模、辨别与控制国际会议 ICMIC 2012)

作者： Xi Chen Wei Wang Wei Li Institute of System engineering Huazhong University of Science & TechnologyImage Processing and Int Candidate for Master of theInstitute of System engineering Huazhong University of Science &Technolog Department of Control Science &Engineering Image Processing and Intelligent Control Key Laboratory o

ISBN: (纸本)9781467315241

The overview presents the development and application of Hierarchical Temporal Memory (HTM).HTM is a new machine learning method which was proposed by Jeff Hawkins in *** is a biologically inspired cognitive method based on the principle of how human brain *** method invites hierarchical structure and proposes a memory-prediction framework, thus making it able to predict what will happen in the near *** overview mainly introduces the developing process of HTM, as well as its principle, characteristics, advantages and applications in vision, image processing and robots movement, some potential applications by using HTM , such as thinking process, are also put forward.

关键词： hierarchical Bayesian network spatial-temporal memory-prediction temporal sequence pattern recognition

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