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IEEE International Conference on Multimedia and Expo (ICME)

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

Video surveillance and object tracking have drawn increased interests in recent years. This paper addresses the problem of moving object tracking from image sequences captured from stationary cameras. Based on the previous work on video segmentation using joint space-time-range mean shift, we extend the scheme to enable the tracking of moving objects. Large displacements of pdf modes in consecutive image frames are exploited for tracking. We also improve the above mean shift-based video segmentation by introducing edge-guided merging of over-segmented regions. This can be viewed as an extension of the enhanced mean shift 2D image segmentation in [2] to the enhanced space-time-range mean shift video segmentation. Experiments have been conducted on several indoor and outdoor videos. Our preliminary results and performance evaluation have indicated the effectiveness of the proposed scheme.

关键词： Tracking Videos image segmentation Merging Kernel Cameras Object detection Change detection algorithms image processing pattern recognition

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Local Structure Detection with Orientation-invariant Radial Configuration

Local Structure Detection with Orientation-invariant Radial ...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Lech Szumilas Rene Donner Georg Langs Allan Hanbury Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

Local image descriptors have proved themselves as useful tools for many computer vision tasks such as matching points between multiple images of a scene and object recognition. Current descriptors, such as SIFT, are designed to match image features with unique local neighborhoods. However, the interest point detectors used with SIFT often fail to select perceptible local structures in the image, and the SIFT descriptor does not directly encode the local neighborhood shape. In this paper we propose a symmetry based interest point detector and radial local structure descriptor which consistently captures the majority of basic local image structures and provides a geometrical description of the structure boundaries. This approach concentrates on the extraction of shape properties in image patches, which are an intuitive way to represent local appearance for matching and classification. We explore the specificity and sensitivity of this local descriptor in the context of classification of natural patterns. The implications of the performance comparison with standard approaches like SIFT are discussed.

关键词： Detectors Shape Computer vision Layout Object recognition

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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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Boosting discriminative model for moving cast shadow detection

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Jisuanji Xuebao/Chinese Journal of Computers 2007年第8期30卷 1295-1301页

作者： Zha, Yu-Fei Chu, Ying Wang, Xun Ma, Shi-Ping Bi, Du-Yan Signal and Information Processing Laboratory Engineering College Air Force Engineering University Xi'an 710038 China Key Laboratory for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

Moving cast shadow causes serious problem while segmenting and extracting foreground from image sequences, due to the misclassification of moving shadow as foreground. This paper proposes a Boosting discriminative model to eliminate cast shadow on Discriminative Random Fields (DRFs). The method combines different features for Boosting to discriminate cast shadow from moving objects, then temporal and spatial coherence of shadow and foreground are incorporated on Discriminative Random Fields and the problem can be solved by graph cut. Firstly, moving objects are obtained by background subtraction;secondly, shadow candidates can be derived through pre-processing moving objects, in terms of the shadow physical property;thirdly, color information and texture information is derived by comparing shadow and foreground points in current image with corresponding points in background image, which are selected as features for Boosting;finally, temporal and spatial coherence of shadow and foreground is employed on Discriminative Random Fields and discriminate shadow and foreground by graph cut accurately.

关键词： image processing

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Normalized Linear Transform for Cross-Platform Microarray Data Integration

Normalized Linear Transform for Cross-Platform Microarray Da...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Huilin Xiong Ya Zhang Xue-wen Chen Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS USA

With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.

关键词： Costs Gene expression Support vector machines Support vector machine classification Machine learning Application software image processing pattern recognition Computer science Statistical analysis

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A Multiphase Car-Following Model of Traffic Flow and Numerical Tests

A Multiphase Car-Following Model of Traffic Flow and Numeric...

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2007 IEEE International Conference on Automation and Lofistics

作者： Zhipeng Li Fuqiang Liu Yuncai Liu Department of Information and Communication Engineering Tongji University Shanghai 201804China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240

ISBN: (纸本)9781424415304

we present in this paper an empirical desired headway model for a single lane car-following theory based on the equation of motion of each *** this new model,traffic situation is suggested to be divided into car-following and free travel regions,and in car-following area the stimulus is a function of space difference between the current headway and the empirical desired headway with a constant *** investigate this model with numerical method and it demonstrates some complex physical features observed in real traffic such as existence of three phases:free,synchronized,and jam flow;spontaneous formation of traffic jams;sudden flow drop in flow- density plane;local cluster effects in synchronized regime;traffic hysteresis in transition between the free and the synchronized ***,the proposed model also predicts that in the synchronized flow regime there exists a unique stable state to relative density,which is insusceptible to noise.

关键词： car-following models inter-vehicle spacing traffic flow

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A Flexible Fully-Multiplicative Orthogonal-Group Based ICA Algorithm

A Flexible Fully-Multiplicative Orthogonal-Group Based ICA A...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Yalan Ye Jing Wan Zhi-Lin Zhang Chen Jia Wu Lei School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai China

In this paper, we propose a flexible fully-multiplicative orthogonal-group based ICA (FlexibleOgICA) algorithm, which can instantaneously separate the mixture of sub-Gaussian and super-Gaussian source signals. It adopts a self-adaptive nonlinear function, which adjusts its parameter to achieve better performance based on the estimation of the kurtosis of super-Gaussian source signals. We also have successfully applied the algorithm to obtain the fetal electrocardiogram (FECG) signal, showing its fast convergence speed and high separation performance

关键词： Independent component analysis Signal processing algorithms Convergence Computational intelligence Bioinformatics Computational biology Signal processing Multidimensional signal processing Biomedical signal processing Switches

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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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A CI feature-based pulmonary nodule segmentation using three-domain mean shift clustering

A CI feature-based pulmonary nodule segmentation using three...

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International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Sheng-Dong Nie Li-Hong LI Zhao-Xue Chen Institute of Medical Imaging Engineering School of Medical Instrumentation & Foodstuff University of Shanghai for Science and Technology Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

A novel and more effective algorithm used for segmenting pulmonary nodules in thoracic spiral CT images was presented. The algorithm is based on mean shift clustering method and CI (Convergence Index) features, which can represent the multiple Gaussian model of pulmonary nodules both for solid and sub-solid, substantially. The algorithm has the following steps: (1) calculating the CI features of all pixels in the region of interest (ROI), (2) combining the CI features with the intensity range and the spatial position of the pixels to form a feature vector set, (3) grouping the feature vector set to clusters with mean shift clustering algorithm. Owing to our algorithm can represent the multiple Gaussian model both for solid and sub-solid nodules, it can be used in any user interested nodule regions, especially suitable for the segmentation of sub-solid nodules. Experiments demonstrated that our algorithm can figure out the outline of pulmonary nodules of different forms more precisely.

关键词： Clustering algorithms image segmentation Lungs Computed tomography Cancer pattern recognition Biomedical imaging Solid modeling Wavelet analysis image analysis

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Remote sensing image matching based on corner structures

Remote sensing image matching based on corner structures

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International Conference on image and Graphics (ICIG)

作者： Hongya Tuo Zhongliang Jing Yuncai Liu Institute of Aerospace Sci. & Tech. Shanghai Jiaotong University Shanghai China Inst. of Image Processing & Pattern Recognition Shanghai Jiaotong University Shanghai China

In this paper, we propose a matching method for remote sensing images based on corner structures. Firstly corner angle vector is defined to analysis corner structure, and the process of how to obtain it is discussed in detail. Then some measures are given to eliminate the false corners. Finally a relaxation matching scheme is presented. The experiments show the effectiveness and feasibility of our matching method.

关键词： Remote sensing image matching Arm Computer vision Detectors Bismuth pattern recognition image edge detection Graphics image processing

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