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Moment Invariants for Color Constant Image Description

电子学报(英文版) 2010年第2期19卷 283-287页

作者： LI Bing XU De LANG Congyan Institute of Computer Science and Engineering Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

An important class of color constant image descriptors is based on image derivatives. However, these derivative-based descriptors are sensitive to blurring changes of image, and also lose much color information in uniform regions. Aiming at overcoming these drawbacks, the contributions of this paper are in three-folds: First, by introducing 3D moment invariants, we propose a color constant descriptor in RGB color space, which can characterize original image color content. Second, we define a 2D edge-based color space based on the diagonal-offset reflectance model. A novel color constant descriptor in the proposed edge-based color space is presented by using moment invariants. This descriptor can characterize edge image with nearly no sensitivity to image blurring. Finally, we combine the aforementioned descriptors to construct the third color constant descriptor, which can describe not only original image color content but also the edge image color feature. The experiments on scene recognition show that all these descriptors have satisfying performance under various light sources. Furthermore, all these proposed descriptors are insensitive to image affine transformations and blurring changes.

关键词：图像描述不变矩彩色 RGB颜色空间恒定模糊图像原始图像色彩空间

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Two alternative approaches to stochastic discrete-time iterative learning control systems

Two alternative approaches to stochastic discrete-time itera...

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IEEE Conference on Decision and Control

作者： Deyuan Meng Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control 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 School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781612848006

This paper aims to address the robust convergence problem that arises from discrete-time iterative learning control (ILC) systems subject to random disturbances. Two alternative approaches are considered in order to achieve the perfect output tracking of the stochastic discrete-time ILC systems in the sense of both expectation and variance, which use the tracking error and the input error for analysis, respectively. It is shown that the convergence results of two approaches to ILC can be established by developing some statistical expressions in super-vector forms. Moreover, it is demonstrated that the convergence results of two approaches to ILC are not always equal, and they can keep the same only in the case where the controlled plants are square.

关键词： Iterative learning control discrete-time systems random disturbances

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Robust L_(2) - L_(infinity) Consensus Control of the Second-order Multi-agent Systems with Time-delay

Robust L_(2) - L_(infinity) Consensus Control of the Second-...

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IEEE Conference on Decision and Control

作者： Yan Cui Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University 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 School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781612848006

Robust L_(2) - L_(infinity) consensus control is studied for the second-order multi-agent systems with external disturbances and parameter uncertainties. By defining an appropriate controlled output, the consensus problem of the systems is transformed into a normal L_(2) - L_(infinity) control problem, and distributed state feedback protocols with time-delay are proposed. Sufficient conditions are established for the convergence to consensus of the network under fixed or switching topology. Numerical simulations are provided to demonstrate the effectiveness of our theoretical results.

关键词： Topology Europe

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Column Formation Control of Multi-robot Systems with Input Constraints

Column Formation Control of Multi-robot Systems with Input C...

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IEEE Conference on Decision and Control

作者： Xiaohan Chen Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control 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 School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781612848006

This paper is concerned with the column formation control of multi-robot systems that are subject to input constraints. A new leader-follower setup is proposed, under which the Lyapunov method can provide a simple controller in terms of two feedback parameters. It is shown that using an elliptic approximation to input constraints, the two feedback parameters can be obtained directly with a geometric analysis method. Moreover, a sufficient condition is presented for the leader robot to guarantee that the desired column formation can be achieved, with which a particular controller is designed by selecting two appropriate functions for the feedback parameters. Simulation results are included to verify the effectiveness of the proposed theoretical results.

关键词： Column formation control multi-robot systems leader-follower input constraints elliptic approximation

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Gaussian mixture PHD filter for multiple maneuvering extended targets tracking

Gaussian mixture PHD filter for multiple maneuvering extende...

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IEEE Conference on Decision and Control

作者： Wenling Li Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control 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 School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781612848006

This paper addresses the problem of tracking multiple maneuvering extended targets in the framework of random finite set theory. An elliptical model is adopted for exploiting sensor measurements of target extent, and the target dynamics is described by a jump Markov linear system which combines the shape parameters into the kinematic state vector. As each extended target gives rise to unlabeled multiple measurements per time step, all the received measurements are partitioned into a number of subsets so that the measurements in each subset are expected to stem from the same source. In addition, the best-fitting Gaussian approximation approach is employed to circumvent the difficulty of multiple model mixing in the Gaussian mixture probability hypothesis density (GM-PHD) filter. A numerical example is provided to compare the performance of the proposed filter with that of the GM-PHD filter without measurement partition.

关键词： Maneuvering Filters shape parameter extended target Gauss Subset numerical example

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Collision Avoidance Maneuver Design Based on Equidistance Interpolation

Collision Avoidance Maneuver Design Based on Equidistance In...

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IEEE Conference on Decision and Control

作者： Yongqiang Qi Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control 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 School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

ISBN: (纸本)9781612848006

This paper deals with the active collision avoidance maneuver for the chaser along the specified trajectory, and presents a maneuver approach with constant thrust. Using the 3D stereo vision measurement, The relative position parameters of the target spacecraft for analyzing the collision possibility are obtained by using the vision measurement and the target maneuver positions are calculated through the equidistance interpolation method. The working times of thrusters in three axes can be respectively computed by the time series analysis method. In particular, the perturbation and fuel consumption are addressed during the computation of working time. Furthermore, the acceleration sequences and the corresponding working time series can be employed to determine a switching control law for the active collision avoidance maneuver. The simulation results show that the switch control law can effectively guarantee the chaser moving along the specified trajectory.

关键词： Constant thrust Vision measurement Switch control law The specified trajectory Equidistance interpolation

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Formation control of mobile robots with input constraints: An elliptic approximation approach

Formation control of mobile robots with input constraints: A...

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International Conference on Control, automation and Systems ( ICCAS)

作者： Xiaohan Chen Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and Department of Systems and Control Beihang University BUAA Beijing China Beihang University Beijing CN Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper considers the problem of formation control for mobile robots which are subject to input constraints. The control strategy based on virtual robot tracking is proposed, which leads to a compact controller form in terms of two feedback parameters by Lyapunov method. The introduced elliptic approximation of input constraints brings much convenience to obtain suitable feedback parameters. It is show that the presented sufficient condition guarantees the existence of feasible feedback parameters. Moreover, guidelines for the selection of feedback parameters are provided with geometric analysis. Simulation results verify the effectiveness of the proposed control strategy.

关键词： Robot kinematics Lead Mobile robots Approximation methods Trajectory Educational institutions

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Adaptive Iterative Learning Control for Uncertain Delay Systems Based on Model Matching Technique

Adaptive Iterative Learning Control for Uncertain Delay Syst...

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2011 American Control Conference (ACC)

作者： Haixia Su Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University Beijing China Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

In this paper, an adaptive iterative learning control (ILC) scheme is proposed for trajectory tracking of uncertain delay systems based on model matching technique. The reference model is a delay system operating over in a finite time interval. An iterative model matching controller is designed and an iteration domain adaptive law is chosen to estimate the unknown parameters. It shows that the model matching technique can be applied in a straightforward method to ILC problem. A simulation example is included to illustrate the designed scheme.

关键词： Adaptation models Polynomials Adaptive systems Delay effects Delay systems

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Gaussian Mixture PHD Smoother for Jump Markov Models in Multiple Maneuvering Targets Tracking

Gaussian Mixture PHD Smoother for Jump Markov Models in Mult...

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2011 American Control Conference (ACC)

作者： Wenling Li Yingmin Jia Junping Du Fashan Yu Seventh Research Division and the Department of Systems and Control Beihang University Beijing China Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan China

This paper presents a Gaussian mixture probability hypothesis density (GM-PHD) smoother for tracking multiple maneuvering targets that follow jump Markov models. Unlike the generalization of the multiple model GM-PHD filters, our aim is to approximate the dynamics of the linear Gaussian jump Markov system (LGJMS) by a best-fitting Gaussian (BFG) distribution so that the GM-PHD smoother can be carried out with respect to an approximated linear Gaussian system. Our approach is inspired by the recognition that the BFG approximation provides an accurate performance measure for the LGJMS. Furthermore, the multiple model estimation is avoided and less computational cost is required. The effectiveness of the proposed smoother is verified with a numerical simulation.

关键词： Target tracking Approximation methods Markov processes Covariance matrix Smoothing methods Radar tracking Computational efficiency

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3D model based vehicle tracking using gradient based fitness evaluation under particle filter framework

3D model based vehicle tracking using gradient based fitness...

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International Conference on Pattern Recognition

作者： Zhang, Zhaoxiang Huang, Kaiqi Tan, Tieniu Wang, Yunhong School of Computer Science and Engineering Beihang University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9780769541099

We address the problem of 3D model based vehicle tracking from monocular videos of calibrated traffic scenes. A 3D wire-frame model is set up as prior information and an efficient fitness evaluation method based on image gradients is introduced to estimate the fitness score between the projection of vehicle model and image data, which is then combined into a particle filter based framework for robust vehicle tracking. Numerous experiments are conducted and experimental results demonstrate the effectiveness of our approach for accurate vehicle tracking and robustness to noise and occlusions. © 2010 IEEE.

关键词： Monte Carlo methods

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