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2007 IET Conference on Wireless, Mobile and Sensor Networks (CCWMSN07)

作者： Q.Q. Shi H. Huo T. Fang R.D. Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Shanghai Jiaotong University Shanghai China

Refinement is a necessary and effective step in some node localization schemes of wireless sensor networks (WSN). Suitable refinement procedure can improve the node localization accuracy and raise the robustness of the localization algorithm. However, most existing refinement algorithms are costly duo to complex computation and frequent communication, and may induce serious coverage problem duo to nonconvergent iterations. In view of above facts, Steepest descent method is proposed to be used as the refinement algorithm in this paper, and corresponding simulation experiments are done to testify its feasibility and validity. The results show that steepest descent method can optimize the node positions to a fairish accuracy extent, and compared with existing refinement methods, it outperforms in communication cost, computation cost, and coverage rate.

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Comparative studies of Feature Extraction methods with application to face recognition

Comparative studies of Feature Extraction methods with appli...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yunfei Jiang Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In face recognition, the dimensionality of raw data is very high, dimension reduction (feature extraction) should be applied before classification. There exist several feature extraction methods, commonly used are principle component analysis (PCA) and linear discriminant analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include eigenfaces, kernel principal component analysis (KPCA), fisherfaces, direct linear discriminant analysis (D-LDA), regularized linear discriminant analysis (R-LDA), and kernel direct discriminant analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt nearest neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones.

关键词： Feature extraction Face recognition Linear discriminant analysis Principal component analysis Kernel pattern recognition Nearest neighbor searches Neural networks Face detection image databases

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Application of "human-in-the-loop" control to a biped walking-chair robot

Application of "human-in-the-loop" control to a biped walkin...

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2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007

作者： Jiaoyan, Tang Qunfei, Zhao Jie, Huang Mechanical Engineering School University of Shanghai Jiao Tong Shanghai 200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China College of Computer Science and Engineering University of Aizu Aizu Wakamatsu 965-8580 Japan

ISBN: (纸本)1424409918

This paper presents the research on stability for biped Walking-Chair robot with human-in-the-loop. The inherent properties of the biped system which is developed for the disable people to replace traditional wheelchairs are analyzed. Control of the robot for the gait and navigation is introduced. Posture stability computation method based on ZMP (Zero Moment Point) theory is discussed. Some suggestions about the future research are also presented. © 2007 IEEE.

关键词： Robots

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Sparse MRF appearance models for fast anatomical structure localisation

Sparse MRF appearance models for fast anatomical structure l...

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2007 18th British Machine Vision Conference, BMVC 2007

作者： Donner, René Mičušsík, Branislav Langs, Georg Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria GALEN Group Laboratoire de Mathématiques Appliquées Aux Systèmes Ecole Centrale de Paris France

ISBN: (纸本)1901725340

image segmentation methods like active shape models, active appearance models or snakes require an initialisation that guarantees a considerable overlap with the object to be segmented. In this paper we present an approach that localises anatomical structures in a global manner by means of Markov Random Fields (MRF). It does not need initialisation, but finds the most plausible match of the query structure in the image. It provides for precise, reliable and fast detection of the structure and can serve as initialisation for more detailed segmentation steps. Sparse MRF Appearance Models (SAMs) encode a priori information about the geometric configurations of interest points, local features at these points and local features along the edges of adjacent points. This information is used to formulate a Markov Random Field and the mapping of the modeled object (e.g. a sequence of vertebrae) to the query image interest points is performed by the MAX-SUM algorithm. The local image information is captured by novel symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.

关键词： image segmentation

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Robot navigation and sound based position identification

Robot navigation and sound based position identification

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huakang Li Satoshi Ishikawa Qunfei Zhao Michiko Ebana Hiroyuki Yamamoto Jie Huang School of computer science and engineering University of Aizu Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

In this paper, we proposed a robot self position identification method by active sound localization. This method can be used for autonomous security robots working in room environments. A system using a AIBO robot equipped with two microphones and wireless network is constructed and is used for position identification experiments. Arrival time differences to the microphones of robot are used as localization cues. To overcome the ambiguity of front-back confusion, a three-head position measurement method was proposed. The robot position can be identified by the intersection of circles restricted by the azimuth differences to different speaker pairs. By localizing three or four speakers as sound beacons positioned on known locations, the robot can identify its self position with an average error of about 7 cm in a 2.5times3.0 m 2 working space. A robot navigation experiment was conducted to demonstrate the effectiveness of the position identification system.

关键词： Navigation Robot sensing systems Microphones Loudspeakers Humans Mobile robots Azimuth Humanoid robots Orbital robotics Auditory system

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Random Feature Subset Selection for Analysis of Data with Missing Features

Random Feature Subset Selection for Analysis of Data with Mi...

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International Joint Conference on Neural Networks (IJCNN)

作者： Joseph DePasquale Robi Polikar Signal Processing and Pattern Recognition Laboratory Electrical and Computer Engineering Rowan University Glassboro NJ USA

We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in part by the incremental learning algorithm, Learn ++ . The premise is to generate an adequately large number of classifiers, each trained on a different and random combination of features, drawn from an iteratively updated distribution. To classify an instance with missing features, only those classifiers whose training data did not include the currently missing feature are used. These classifiers are combined by using a majority voting combination rule to obtain the final classification of the given instance. We had previously presented preliminary results on a similar approach, which could handle up to 10% missing data. In this study, we expand our work to include different types of rules to update the distribution, and also examine the effect of the algorithm's primary free parameter (the number of features used to train the ensemble of classifiers) on the overall classification performance. We show that this algorithm can now accommodate up to 30% of features missing without a significant drop in performance.

关键词： Data analysis Neural networks Training data Clustering algorithms Fuzzy neural networks USA Councils Algorithm design and analysis Iterative algorithms Random number generation Voting

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ML Nonlinear Smoothing for image Segmentation and its Relationship to the Mean Shift

ML Nonlinear Smoothing for Image Segmentation and its Relati...

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IEEE International Conference on image processing

作者： Andy Backhouse Irene Y. H. Gu Tiesheng Wang Department of Signals and Systems Chalmers University of Technology Sweden Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

This paper addresses the issues of nonlinear edge-preserving image smoothing and segmentation. A ML-based approach is proposed which uses an iterative algorithm to solve the problem. First, assumptions about segments are made by describing the joint probability distribution of pixel positions and colours within segments. Based on these assumptions, an optimal smoothing algorithm is derived under the ML condition. By studying the derived algorithm, we show that the solution is related to a two-stage mean shift which is separated in space and range. This novel ML-based approach takes a new kernel function. Experiments have been conducted on a range of images to smooth and segment them. Visual results and evaluations with 2 objective criteria have shown that the proposed method has led to improved results which suffer from less over-segmentation than the standard mean-shift.

关键词： Smoothing methods image segmentation Maximum likelihood estimation Iterative algorithms Filters Kernel Stochastic processes Robustness Signal processing image processing

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Object Tracking using Incremental 2D-PCA Learning and ML Estimation

Object Tracking using Incremental 2D-PCA Learning and ML Est...

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

作者： Tiesheng Wang Irene Y.H. Gu Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University China Department of Signals and Systems Chalmers University of Technology Sweden

Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional principal component analysis)-based method for characterizing objects given the tracked regions, and a ML (maximum likelihood) blob-tracking process given the object characterization and the previous blob sequence. The proposed incremental 2DPCA updates the row- and column-projected covariance matrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blob-tracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of single moving object in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.

关键词： Maximum likelihood estimation Principal component analysis Covariance matrix Target tracking Shape Video surveillance image processing pattern recognition Signal processing Recursive estimation

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A Services Selection Policy for ServiceBSP Model with QoS-aware in Grids

A Services Selection Policy for ServiceBSP Model with QoS-aw...

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International Conference on Convergence Information Technology

作者： Yan Jiang Weiqing Tong Wentao Zhao School of Computer Engineering and Science Shanghai University China Image Processing and Pattern Recognition Institute Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424431779

ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in grid environment where large-scale and geographically distributed resources (abstracted as services) are available. In this paper, we advocate a preferable service broker framework to support the services selection policy that could optimize the services selection for ServiceBSP model. It means that the services selection can increase the level of satisfaction measured by overall quality value and decrease execution time by considering services dependent relation and location in a subtask group. Meanwhile, the policy is preliminary work ensuring reasonable distribution of services and loading balance in ServiceBSP model. In an addition, we give a detailed process of services selection based on heuristic greedy algorithm. Finally, by comparing response time and price using proposed policy with other policy only considering QoS, the experimental simulation shows that our services selection policy possesses superior performance on response time and then provides satisfactory result for users.

关键词： Quality of service Ontologies Large-scale systems Delay Information technology Grid computing pattern recognition Costs Time measurement Greedy algorithms

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Camera Calibration for 3D Color Scanning System Using Virtual 3D Model

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Computer Aided Drafting,Design and Manufacturing 2007年第2期17卷 77-81页

作者： SUN Xian-bin LI De-hua YIN Jie YAO Xun State Key Laboratory for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Hubei University of Technology Wuhan 430068 China

Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control points with variable Z values. Experiments show that the approach presented is effective for reconstructing 3D color objects in computer vision system.

关键词： computer vision 3D scan camera calibration calibration plate

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