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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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image denoising algorithm based on noise detection

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Moshi Shibie yu Rengong Zhineng/pattern recognition and Artificial Intelligence 2008年第3期21卷 298-302页

作者： Jin, Liang-Hai Li, De-Hua State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong Univ. of Sci. and Technol. Wuhan 430074 China

An efficient image denoising algorithm is introduced. Firstly, image pixels are classified into noisy pixels and noise-free pixels by four directional operators. Then an adaptive weighted median filter is designed to remove and restore the detected noisy pixels and keep the noise-free ones unchanged. Experimental results indicate that the proposed algorithm preserves image details well while removing impulsive noise efficiently, and its filtering performance is significantly superior to the classical median filter and some other typical and recently developed improved median filters.

关键词： Median filters

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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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Pedestrian detection using hybrid statistical feature

Pedestrian detection using hybrid statistical feature

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IEEE Workshop on Multimedia Signal processing

作者： Qiang Wu Chunhua Du Jie Yang Xiangjian He Yan Chen School of Computing and Communication University of Technology Sydney Australia Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424422944;9781424422951

A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA gait database and the additional non-human objects data.

关键词： Humans Reactive power Support vector machines Feature extraction Pixel Training Support vector machine classification

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Method of boundary extraction based on schrödinger equation 21

Method of boundary extraction based on schrödinger equation

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2008 21st ISPRS International Congress for Photogrammetry and Remote Sensing

作者： Lou, Liantang Zhan, Xin Fu, Zhongliang Ding, Mingyue School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China School of Science Wuhan Institute of Technology Wuhan430073 China Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.

关键词： Initial value problems

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Registration and enhancing of multispectral manuscript images

Registration and enhancing of multispectral manuscript image...

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European Signal processing Conference (EUSIPCO)

作者： Martin Lettner Markus Diem Robert Sablatnig Heinz Miklas Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria Institute for Slavonic Studies University of Vienna Vienna Austria

Two medieval manuscripts are recorded, investigated and analyzed by philologists in collaboration with computer scientists. Due to mold, air humidity and water the parchment is partially damaged and consequently hard to read. In order to enhance the readability of the text, the manuscript pages are imaged in different spectral bands ranging from 360 to 1000nm. A registration process is necessary for further image processing methods which combine the information gained by the different spectral bands. Therefore, the images are coarsely aligned using rotationally invariant features and an affine transformation. Afterwards, the similarity of the different images is computed by means of the normalized cross correlation. Finally, the images are accurately mapped to each other by the local weighted mean transformation. The algorithms used for the registration and results in enhancing the texts using Multivariate Spatial Correlation are presented in this paper.

关键词： Correlation Principal component analysis Remote sensing Cameras Eigenvalues and eigenfunctions Europe Signal processing

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Needle segmentation using 3D quick randomized hough transform

Needle segmentation using 3D quick randomized hough transfor...

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1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Institute for Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9780769533919

A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D Quick Randomized Hough Transform (3DGHT), which is based on the 3D Randomized Hough Transform and coarse-fine searching strategy. We tested it with water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1 and position deviation less than 1mm, and the computational time of segmentation with 35MB data is within 1s. © 2008 IEEE.

关键词： Hough transforms

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