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Path planning for UAV based on quantum-behaved particle swarm optimization

Path planning for UAV based on quantum-behaved particle swar...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and pattern recognition

作者： Fu, Yangguang Ding, Mingyue Zhou, Chengping Cai, Chao Sun, Yangguang Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478085

Based on quantum-behaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are presented and compared through a UAV path planning application. Every particle in swarm represents a potential path in search space. For the purpose of pruning the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. As the system iterated, each particle is pulled toward its local attractor, which is located between the personal best position (pbest) and the global best position (gbest) based on the interaction of particles' individual searches and group's public search. For the sake of simplicity, we only consider planning the projection of path on the plane and assume threats are static instead of moving. Simulation results demonstrated the effectiveness and feasibility of the proposed approach. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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The optical-flow aided navigation for the INS

The optical-flow aided navigation for the INS

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MIPPR 2009 - Automatic Target recognition and image Analysis: 6th International Symposium on Multispectral image processing and pattern recognition

作者： Pan, Chao Liu, Jianguo Xiao, Wei State Key Laboratory for Multispectral Information Processing Technologies Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China

ISBN: (纸本)9780819478061

The inertial navigation system (INS) is an efficient navigation method but it can occur accumulative errors. To correct this error in the INS, this paper presents a novel optical-flow aided navigation method by studying the terrain aided navigation, which can implement the continuous error estimation of the INS by employing the extended kalman filter that regards optical-flow as its measurement parameter. Therefore, this aided method is similar to the terrain aided navigation except that the measurement parameter and equation are both derived from optical-flow instead of terrain height. Experiments with some actual aerial image sequences have proven the efficiency of this aided navigation method. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Kalman filters

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A flower algorithm for autonomous star pattern recognition

A flower algorithm for autonomous star pattern recognition

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作者： Gong, Jiaqi Ma, Jie Tian, Jinwen State Key Laboratory for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781563479694

An autonomous star pattern recognition algorithm is proposed herein that is effectively stable against the higher level noise than other algorithms. In simulations using an 8×8 degree field of view (FOV), the algorithm identifies the correct section of sky on 99.5% of the sensor orientations where spatial accuracy of the imaged star is 2 pixels (0.03 degree) in standard deviation and the apparent brightness deviate by 0.5 units' stellar magnitude. The achievable performance comes from the generation of the Flower-code pattern, which includes angular distances and circular angles as the characteristics of the pivot star. The angular distances are used for initial lookup table match. Moreover, the circular angles are adopted for the cyclic dynamic match between the sensor pattern and the pattern candidates from the initial match. And also, the performance is demonstrated via a comparison study on two published algorithms. Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Table lookup

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A novel algorithm for small dim target detection in distorted infrared image sequence

A novel algorithm for small dim target detection in distorte...

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MIPPR 2009 - Automatic Target recognition and image Analysis: 6th International Symposium on Multispectral image processing and pattern recognition

作者： Qu, Xiaolei Zuo, Zhengrong Li, Xiafeng Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multispectral Information Processing Technologies Huazhong University of Science and Technology Wuhan Hubei 430074 China

ISBN: (纸本)9780819478061

A novel algorithm is proposed in this paper to detect dim moving point target with several pixels size in infrared image sequence, taken by line scan camera in high speed moving platform, with heavy background clutter and distortion. In the algorithm, the regional information of candidate points, which are extracted from the images after background suppression by spatial filter, is employed to track candidate points. After tracking candidate points, the velocity of every candidate points can be calculated easily, and false candidate points which are extracted from clutter background, can be find out by velocity classification and removed. Then, Double Hough Transform is applied on associating the remnant candidate points in multi-frame sequence to detect the target. The algorithm can get a high detection probability and reduce false alarm rate dramatically. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Hough transforms

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Global systolic array for fast computation of 3-D moment invariants

Global systolic array for fast computation of 3-D moment inv...

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MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and pattern recognition

作者： Liu, J.G. Chen, Shaobo Wang, Hai State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology WuHan HuBei 430074 China

ISBN: (纸本)9780819478085

The 3-D moment invariants are introduced to solve the problem of recognition of 3-D objects independent of size, position, and orientation. In this paper, based on our previous systolic array for fast computation of 3-D moment, we propose a new global systolic array for fast computation of 3-D moment invariants. The systolic sarray mostly consists of adders with area complexity O(n) and are highly regular and structurally very simple, resulting in simple hardware implementation. The method is suitable for both binary images and gray level images and is also suitable for image sequence moments. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Systolic arrays

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A Linear Approach to Camera Calibration Using Concentric Circles

A Linear Approach to Camera Calibration Using Concentric Cir...

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International Conference on Information Technology and Computer Science, ITCS

作者： Wenjuan Ma Yinqiang Zheng Yuncai Liu Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

In this paper, we present a new algorithm for camera calibration using concentric circles, which is a linear approach. Different from previous methods, we take the projective equations of three-dimensional circles, which include the intrinsic parameter matrix of the camera, as the basis of our calibration approach. According to the special structure of the projective equations in algebra, we can get a linear equation system about the intrinsic parameters. After enough equations constructed, the calibration can be easily finished. Our method needs three images of the two concentric circles at least, and all the five intrinsic parameters can be recovered. Experiments using computer simulated data demonstrate the robustness and accuracy of our method.

关键词： Cameras Calibration Equations Transmission line matrix methods Matrix decomposition Algebra Robustness Information technology Computer science image processing

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Missing traffic flow data prediction using least squares support vector machines in urban arterial streets

Missing traffic flow data prediction using least squares sup...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Yang Zhang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arterial streets are incomplete, and the importance of effectively imputing the missing values emerges. In this paper, a technique called least squares support vector machines (LS-SVMs) is introduced to predict missing traffic flow based on spatio-temporal analysis in urban arterial streets. To the best of our knowledge, it is the first time to apply the rising computational intelligence (CI) technique incorporating with state space approach in missing traffic data imputation. Having good generalization ability and guaranteeing global minima ensure its well performance in the area. A baseline imputation technique, expectation maximization/data augmentation (EM/DA), is selected for comparison because of its proved effectiveness in missing data recovery. Through persuasive comparisons of the techniques, the proposed model is proved to be more applicable and performs better in stability and adaptability, which reveals that it is a promising approach in missing data prediction.

关键词： Least squares methods Support vector machines Intelligent transportation systems Machine intelligence Competitive intelligence Detectors Computational intelligence State-space methods Predictive models Stability

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Robust 3D modeling from silhouette cues

Robust 3D modeling from silhouette cues

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Enliang Zheng Qiang Chen Xiaochao Yang Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

We consider the problem of 3D modeling under the environments where colors of the foreground objects are similar to the background, which poses a difficult problem of foreground and background classification. A purely image-based algorithm is adopted in this paper, with no prior information about the foreground objects. We classify foreground and background by fusing the information at the pixel and region levels to obtain the similarity probability map, followed by a Bayesian sensor fusion framework to infer the space occupancy grid. The estimation of the occupancy allows incremental updating once a new observation is available, and the contribution of each observation can be adjusted according to its reliability. Finally, three parameters in the algorithm are analyzed in detail and experiments show the effectiveness of this method.

关键词： Robustness Bayesian methods image reconstruction Cameras Probability Pixel image processing pattern recognition Color Sensor fusion

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Deformable Surface 3D Tracking Using Linear Programming

Deformable Surface 3D Tracking Using Linear Programming

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International Workshop on Computer Science and Engineering (WCSE)

作者： Chenhao Wang Shuhan Shen Yuncai Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

A method for 3D shape reconstruction of deformable surfaces from consecutive frames was presented. In our method, the model of the surface is represented by a triangulated mesh. The constraints for the model, including keypoint correspondences and disallowing large changes of edge orientation between consecutive frames, are formulated as Linear Programming (LP) constraints. Therefore the deformable surface 3D tracking method turns into an LP problem that can be effectively solved. The robustness and efficiency of our approach are validated on synthetic and real data.

关键词： Linear programming Surface reconstruction Shape image reconstruction Cameras Deformable models Computer science image processing pattern recognition Robustness

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A Unified Framework for Kernelization: The Empirical Kernel Feature Space

A Unified Framework for Kernelization: The Empirical Kernel ...

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Chinese Conference on pattern recognition (CCPR)

作者： Huilin Xiong Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose to kernelize linear learning machines, e.g., PCA and LDA, in the empirical kernel feature space, a finite-dimensional embedding space, in which the distances of the data in the kernel feature space are preserved. The empirical kernel feature space provides a unified framework for the kernelization of all kinds of linear machines: performing a linear machine in the finite-dimensional empirical feature space, its nonlinear kernel machine is then established in the original input data space. This method is different from the conventional kernel-trick based kernelization, and more importantly, the final nonlinear kernel machines, called empirical kernel machines, are shown to be more efficient in many real-world applications, such as face recognition and facial expression recognition, than the kernel-trick based kernel machines.

关键词： Kernel Face recognition pattern analysis pattern recognition Machine learning Linear discriminant analysis Training data Principal component analysis image processing Support vector machines

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