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4th International Workshop on Advanced Computational Intelligence, IWACI 2011

作者： Zhao, Bo Zhang, Wensheng State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781612843735

A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF is an effective statistical framework to model prior knowledge of natural images which leads to excellent performance in some low-level vision problems. In our work, the framework is extended to image deblurring. To overcome some limitations of maximum a-posteriori (MAP) estimation, we adopt Bayesian minimum mean squared error (MMSE) estimation to perform deblurring. The high-order NLR-MRF prior can be easily integrated into this framework. Then, an efficient Gibbs sampling algorithm is employed to compute MMSE estimation. The proposed method frees the user from determining regularization parameter beforehand, which relies on unknown noise level. Our deblurring method shows superior or comparable results to the state-of-art deblurring methods. © 2011 IEEE.

关键词： Mean square error

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The strategies of avoiding energy holes in wireless sensor networks

The strategies of avoiding energy holes in wireless sensor n...

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4th International Congress on Image and Signal Processing, CISP 2011

作者： Wang, Zhengjie Qian, Xu Zhao, Xiaoguang Beijing China State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424493067

Wireless sensor networks are characterized by multihop network. Some nodes in network are required to forward a disproportionately high amount of traffic and die early, leaving the unmonitored areas in network and leading to the problem of energy hole. This paper investigates a variety of strategies to avoid the energy hole, such as communication power control, data aggregation, nonuniform energy distribution, mobile node auxiliary and clustering. The analysis and comparison of different strategies are given and the advantages and disadvantage of them are discussed in this paper. © 2011 IEEE.

关键词： Sensor nodes

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New method for recognition of circular traffic sign based on radial symmetry and pseudo-zernike moments

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Journal of Beijing Institute of Technology (English Edition) 2011年第4期20卷 520-526页

作者： Fu, Meng-Yin Huang, Yuan-Shui Ma, Hong-Bin Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing 100081 China

Recognizing various traffic signs, especially the popular circular traffic signs, is an essential task for implementing advanced driver assistance system. To recognize circular traffic signs with high accuracy and robustness, a novel approach which uses the so-called improved constrained binary fast radial symmetry (ICBFRS) detector and pseudo-zernike moments based support vector machine (PZM-SVM) classifier is proposed. In the detection stage, the scene image containing the traffic signs will be converted into Lab color space for color segmentation. Then the ICBFRS detector can efficiently capture the position and scale of sign candidates within the scene by detecting the centers of circles. In the classification stage, once the candidates are cropped out of the image, pseudo-zernike moments are adopted to represent the features of extracted pictogram, which are then fed into a support vector machine to classify different traffic signs. Experimental results under different lighting conditions indicate that the proposed method has robust detection effect and high classification accuracy. © Copyright.

关键词： Support vector machines

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A closed-loop method to generate fast C-start for a robotic fish

A closed-loop method to generate fast C-start for a robotic ...

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2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011

作者： Su, Zongshuai Yu, Junzhi Tan, Min Zhang, Jianwei State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Informatics University of Hamburg Germany

ISBN: (纸本)9781424481149

This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed preparatory stage, and the turning precision is achieved by the feedback of the turning angle from a gyroscope and a new design of the propulsive stage. Different types of C-starts are studied, due to the different sizes of caudal fins, in order to achieve the highest turning angular velocity. All the proposed types of C-starts are experimented and compared using a four-joint robotic fish. The experimental results show a fastest angular velocity up to 200°/s and the distinctions between all these different types. © 2011 IEEE.

关键词： Fish

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Vulnerability analysis of power grid based on multi-agent complex systems

Vulnerability analysis of power grid based on multi-agent co...

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2011 IEEE International Conference on Service Operations, Logistics and Informatics, SOLI 2011

作者： Dong, Xisong Xiong, Gang Hou, Jiachen Dong, Fan Nyberg, Timo R. State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China BIT Research Centre Aalto University Aalto Finland

ISBN: (纸本)9781457705731

Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory, based on holism, has its specific advantages in power grid's research. But, it has some limitations. In this article, we improve complex grid by introducing new parameters which can describe the grid's characters better and using multi-agent theory. As an application, the complex power grid constructed with actual data from North China grid is constructed and its vulnerability has been simulated and analyzed under different attacks. © 2011 IEEE.

关键词： complex networks

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Quadratic-Chi Similarity Metric Learning for Histogram Feature

Quadratic-Chi Similarity Metric Learning for Histogram Featu...

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第一届亚洲模式识别会议

作者： Rongguo Zhang Xinyuan Cai Chunheng Wang Baihua Xiao State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio

ISBN: (纸本)9781457701221

Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.

关键词： histogram feature metric learning Quardic-chi similarity face identification

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A Local Learning Based Image-To-Class Distance for Image Classification

A Local Learning Based Image-To-Class Distance for Image Cla...

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第一届亚洲模式识别会议

作者： Xinyuan Cai Rongguo Zhang Baihua Xiao Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Image-To-Class distance is first proposed in Naive- Bayes Nearest-Neighbor. NBNN is a feature-based image classifier, and can achieve impressive classification accuracy. However, the performance of NBNN relies heavily on the large number of training samples. If using small number of training samples, the performance will degrade. The goal of this paper is to address this issue. The main contribution of this paper is that we propose a robust Image-to-Class distance by local learning. We define the patch-to-class distance as the distance between the input patch to its nearest neighbor in one class, which is reconstructed in the local manifold space;and then our image-toclass distance is the sum of patch-to-class distance. Furthermore, we take advantage of large-margin metric learning framework to obtain a proper Mahalanobis metric for each class. We evaluate the proposed method on four benchmark datasets: Caltech, Corel, Scene13, and Graz. The results show that our defined Image-To-Class Distance is more robust than NBNN and Optimal-NBNN, and by combining with the learned metric for each class, our method can achieve significant improvement over previous reported results on these datasets.

关键词： local learning image-to-class distance large margin metric learning Na(i)ve Bayes Nearest-Neighbor image classification

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Contextual Constrained Independent Component Analysis Based Foreground Detection for Indoor Surveillance

Contextual Constrained Independent Component Analysis Based ...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Baihua Xiao Shuang Liu Chunheng Wang State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Recently, Independent Component Analysis based foreground detection has been proposed for indoor surveillance applications where the foreground tends to move slowly or remain still. Yet such a method often causes discrete segmented foreground objects. In this paper, we propose a novel foreground detection method named Contextual Constrained Independent Component Analysis (CCICA) to tackle this problem. In our method, the contextual constraints are explicitly added to the optimization objective function, which indicate the similarity relationship among neighboring pixels. In this way, the obtained de-mixing matrix can produce the complete foreground compared with the previous ICA model. In addition, our method performs robust to the indoor illumination changes and features a high processing speed. Two sets of image sequences involving room lights switching on/of and door opening/closing are tested. The experimental results clearly demonstrate an improvement over the basic ICA model and the image difference method.

关键词： foreground detection contextual constraints indoor surveillance independent component analysis

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Background Modeling by Exploring Multi-scale Fusion of Texture and Intensity in complex Scenes

Background Modeling by Exploring Multi-scale Fusion of Textu...

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第一届亚洲模式识别会议

作者： Zhong Zhang Wen Zhou Chunheng Wang Baihua Xiao Shuang Liu State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automatio State Key Laboratory of Intelligent Control and Management of Complex SystemsInstitute of Automation

ISBN: (纸本)9781457701221

Background modeling is a fundamental yet challenging issue in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale framework, which combines both texture and intensity feature, to achieve a robust and accurate solution. Our contributions are three folds: first, we provide a multi-scale analysis for the issue;second, for texture feature we propose a novel texture operator named Scale-invariant Centersymmetric Local Ternary Pattern, and a corresponding Pattern Adaptive Kernel Density Estimation technique for its probability estimation;third, we design a Simplified Gaussian Mixture Models for intensity feature. Our method is tested on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. The experimental results clearly demonstrate that our method is superior to the previous methods.

关键词： background modeling multi-scale fusion texture intensity

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Translational offset detection based on far-field intensity for grating tiling

Translational offset detection based on far-field intensity ...

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2011 IEEE International Conference on Mechatronics and Automation, ICMA 2011

作者： Huang, Yanlong Fang, Zaojun Xu, De Tan, Min Xia, Lan State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Laboratory of Shanghai Institute of Laser and Plasma Chinese Academy of Engineering Physics Shanghai China

ISBN: (纸本)9781424481149

The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search for each focal spot's centre, feature extraction and intensity computation. Each focal spot's centre can be found with the projections. A self-growing method is used for feature extraction, where the threshold value depends on the gray value of each focal spot's centre. For each focal spot, the sum of gray values within the relevant domain is taken to be its energy intensity. Furthermore, the energy ratio of left and main focal spots (or main and right focal spots) is computed. A formula that expresses the piston distance between two gratings as the function of the energy ratio is fitted with several measured points. Based on this formula, the piston distance is obtained for a new energy-ratio. Finally, the proposed method is verified with a series of experiments. © 2011 IEEE.

关键词： Image processing

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