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2008 Chinese Conference on pattern recognition, CCPR 2008

作者： Lv, Xiaowei Kong, Qing-Jie Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424423163

Human matching is fundamental in human tracking over non-overlapping cameras. Fusing multiple features is an efficient way to increase the ratio of matching. In this paper, we present an algorithm of Iterative Widening Fusion (IWF) to fuse the multiple features, including Color Histogram, UV Chromaticity, Major Color Spectrum Histogram and Scale-Invariant Features (SIFT). Also, the Bayesian framework, as a classical fusion method, is compared with the IWF algorithm. The experimental results indicated that the IWF algorithm obtained the matching accuracy better than Bayesian framework in most cases. ©2008 IEEE.

关键词： Iterative methods

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Subspace analysis methods plus motion history image for human action recognition

Subspace analysis methods plus motion history image for huma...

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Digital image Computing: Techniques and Applications, DICTA 2008

作者： Du, Chunhua Wu, Qiang Yang, Jie He, Xiangjian Chen, Yan School of Computing and Communications University of Technology Sydney Sydney PO Box 123 Broadway 2007 Australia Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 P.R. China

ISBN: (纸本)9780769534565

This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method. © 2008 IEEE.

关键词： Motion analysis

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Fast interactive volume rendering method for adjustable vessel segmentation visualization

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Journal of Shanghai University(English Edition) 2008年第3期12卷 240-248页

作者： MAXIME Guilbot 杨新 Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 P. R. China

Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities. With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint （GPU） shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.

关键词： volume rendering coronary vessels segmentation segmentation error detection texture shader graphic processinguint （GPU）

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A Portable High Resolution Imaging System for Digitizing Large-Surface Paintings

A Portable High Resolution Imaging System for Digitizing Lar...

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Congress on image and Signal processing, CISP

作者： Florian Kleber Robert Sablatnig Pattern Recognition and Image Processing Group Institute for Computer Aided Automation University of Technology Vienna Austria

Digitizing large-surface paintings at a high resolution in museums is necessary in the field of painting conservation to document the actual condition of paintings (e.g. colour measurements) and for analysis (e.g. to analyse underdrawings in high-resolution infrared images). Hence, this paper presents a portable system that is able to scan large-surface paintings or images to provide high-resolution digital images. Additional a 3D-model of the surface of the scanned object is generated. The purpose of the 3D-model is to use the 3D-information to create a planar representation of a once planar painting or image that was deformed through environmental effects. Otherwise uncorrected images lead to blurring in the overlap region of the blended subimages. The resolution of the final image is up to 33 pixel/mm and the size of the scanned object is up to 1.5 m x 1.3 m.

关键词： High-resolution imaging Painting Cameras Hardware image resolution image analysis Digital images Fasteners image processing pattern analysis

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An Improved Random Sampling LDA for Face recognition

An Improved Random Sampling LDA for Face Recognition

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Congress on image and Signal processing, CISP

作者： Yunfei Jiang Xinyu Chen Ping Guo Hanqing Lu Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Linear Discriminant Analysis (LDA) is one of the most used feature extraction techniques for face recognition. However, it often suffers from the small sample size problem with high dimension setting. Random Subspace Method (RSM) is a popular combining technique to improve weak classifier. Nevertheless, it remains a problem how to construct an optimal random subspace for discriminant analysis. In this paper, we propose an improved random sampling LDA for face recognition. Firstly, AdaBoost is adopted to select Gabor feature and remove redundant information. Secondly, in the selected Gabor feature space, we combine principal component analysis and RSM approaches to construct optimal random subspaces for LDA. After that, direct LDA (D-LDA) and R-LDA is applied in each subspace, respectively. Final results are obtained by combining all the LDA classifiers using a fusion rule. Experiments with both the ORL and FERET face databases demonstrate the effectiveness of our proposed method, and it shows promising results compared with previous approaches.

关键词： Sampling methods Linear discriminant analysis Face recognition Principal component analysis pattern recognition Laboratories Feature extraction Scattering Signal processing image sampling

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Automated stroke ending analysis for drawing tool classification

Automated stroke ending analysis for drawing tool classifica...

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International Conference on pattern recognition

作者： Maria C. Vill Robert Sablatnig Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

ISBN: (纸本)9781424421749

This paper proposes a drawing tool recognition method based on features calculated from the shape of stroke endings. The application for this method is to help art historians to identify the drawing tool used for a drawing. Since the style of a drawing depends on the drawing tool used, drawing tool recognition is an important step toward a style analysis. A dominant feature of a drawn stroke is its ending. Several features regarding curvature, proportions etc. are calculated out of the shape of the endings. These features are then used to classify stroke endings with a SVM classifier.

关键词： Skeleton Shape pattern recognition Art Painting image segmentation Gray-scale Joining processes image analysis pattern analysis

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Moving object detection based on blob analysis

Moving object detection based on blob analysis

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IEEE International Conference on Automation and Logistics

作者： Tao Jia Nong-liang Sun Mao-yong Cao Institute of Image Processing and Pattern Recognition College of Information & Electrical Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9781424425020

A single-mode background model based on blob analysis is proposed to segment foreground from image sequences in complex environment. Firstly, the symmetric difference is used to extract a rough moving object. Secondly, blob analysis is utilized to update background model .Finally, classification strategy (block-level and frame-level) is used to extract foreground accurately and avoid the affect of noise and illumination variance. Experimental results show that the presented approach works well in the presence of complex environment and illumination variance.

关键词： Lighting Analytical models Mathematical model Pixel Adaptation model Brightness Noise

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MLPP: A modified version of locality preserving projection

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Journal of Computational Information Systems 2008年第5期4卷 1891-1896页

作者： Zheng, Zhonglong Zhang, Haoran Yang, Fa Yang, Jie Department of Computer Science Zhejiang Normal University Jinhua 321004 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

Locality Preserving Projection (LPP), as a linear manifold learning algorithm, has attracted much interests in recent years. LPP considers an n1× n2image as a vector in €n1×n2space, and thus is limited by the curse of dimensionality. To deal with such problem, Principal Component Analysis (PCA) is traditionally used for dimensionality reduction first. However, some discriminant information would be discarded by PCA. In this paper, a modified version of LPP (MLPP) is proposed which can be applied directly in the original face space without the aid of PCA. Moreover, the mapping matrix derived from MLPP is orthogonal in contrast to that of LPP. Experiments show competitive performance of our method. © 2008 Binary Information Press.

关键词： Principal component analysis

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Moment based level set method for image segmentation

Moment based level set method for image segmentation

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IEEE International Conference on image processing

作者： Juan Zhou Lixiu Yao Jie Yang Chunming Li Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Institute of Imaging Science Vanderbilt University Nashville TN USA

This paper presents a novel level set method for image segmentation. Gray-level moments are used to estimate two fitting functions that approximate local intensities on the two sides of object boundaries, which are then incorporated into a variational level set framework. An energy functional is defined on a contour, which characterizes the approximation of local intensities on the two sides of the contour by the two fitting functions. This energy can be minimized when the contour is on the object boundary. Thus, image segmentation is performed by minimizing this energy functional. A desirable feature of our method is that it is not sensitive to the contour initialization. Moreover, our method is able to segment images with intensity inhomogeneity. Only a small number of iterations are needed to obtain the final result, which makes our method more efficient than previous level set methods.

关键词： Level set image segmentation Active contours image processing pattern recognition Computational modeling Robustness Computational efficiency Convolution image edge detection

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SVM based ASM for facial landmarks location

SVM based ASM for facial landmarks location

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8th IEEE International Conference on Computer and Information Technology (CIT 2008), vol.1

作者： Chunhua Du Qiang Wu Jie Yang Zheng Wu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Department of Computer Systems University of Technology Sydney Australia

Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence.

关键词： Active shape model (ASM) Support vector machine (SVM) Multi-class classification Facial landmark New position

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