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Perceptual Contrast-Based Image Fusion: A Variational Approach

自动化学报 2007年第2期33卷 132-137页

作者： WANG Chao YE Zhong-Fu Institute of Statistical Signal Processing Department of Electrical Engineering and Information Science University of Science and Technology of China Hefei 230027 P.R.China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 P.R.China

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份... 详细信息

本地对比，或变化，在主要是从来源图象保存重要信息到熔化结果的图象熔化起一个重要作用。威伯的法律告诉我们在不同背景下面的一样的变化将引起不同感性的感情，因此，一个理想的图象处理器不得不考虑视觉心理学和心物学的效果。这份报纸考虑人的视觉系统(HVS ) 的性质并且从每幅来源图象把量的感性的变化转移到结果。把 just-noticeable-difference (JND ) 用作测量， multiband 图象的感性的对比作为一个目标被获得。我们构造一个功能的极值问题发现一幅单身的乐队图象，或熔化结果，它有最近感性的对比到目标一个。经由变化途径， Euler-Lagrange 方程被导出，并且一次坡度降下重复被采用。试验性的结果证明这个方法感性地好。

关键词：表象聚变变化方法知觉衬比度 HVS PDES 人工智能

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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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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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Face recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

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IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms pattern recognition Artificial intelligence Particle swarm optimization Computational intelligence Computer security Application software Image processing

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Face recognition Research Based on Anti-Symmetrical Wavelet and Eigenface

Face Recognition Research Based on Anti-Symmetrical Wavelet ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qian Yin Zhi-Yong Yuan Ying Kong Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science Wuhan University of China Wuhan China School of Computer Science & Technology Beijing Institute of Technology Beijing China

In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade the face image dimension, meanwhile complete the process of face location and segmentation; And then human face is reverted through the face space of eigenface, the traditional average human face is replaced in the within-class scatter matrix. This within-class scatter matrix is used to calculate within-class and between-class distance proportion as a rule function, calculate the twice eigenface through discrete Karhunen-Loeve transform (DKLT), and use singular value decomposition (SVD) method to calculate the eigenvector. Finally we compute the weights and classify the face images. The results show that the proposed method has higher recognition rate and more robust than the traditional eigenface analysis method.

关键词： Face recognition Scattering Matrix decomposition Image coding Humans Matrices Degradation Robustness Wavelet analysis Fourier transforms

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Ensemble Based Data Fusion from Parietal Region Event Related Potentials for Early Diagnosis of Alzheimer's Disease

Ensemble Based Data Fusion from Parietal Region Event Relate...

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International Joint Conference on Neural Networks (IJCNN)

作者： Brian A. Balut Matthew T. Karnick Deborah Green John Kounios Christopher M. Clark Robi Polikar Signal Processing and Pattern Recognition Laboratory of the Electrical and Computer Engineering Department Rowan University Glassboro NJ USA Department of Psychology Drexel University Philadelphia PA USA Department of Neurology University of Pennsylvania Philadelphia PA USA

As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.

关键词： Alzheimer's disease Enterprise resource planning Hospitals Electrodes Senior citizens Biomarkers Protocols Delay Neural networks USA Councils

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New intra prediction mode for interlaced video in AVS

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Gaojishu Tongxin/Chinese High Technology Letters 2007年第3期17卷 221-225页

作者： Chen, Liang Tan, Yihua Tian, Jinwen State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

The technologies of intra prediction and MBAFF were introduced, and a new intra prediction mode based on the characteristics of spatial distribution in interlaced video was proposed. The spatial correlation of five luma intra prediction modes in AVS-P2 and the new mode were analyzed. From the analysis result, it can be concluded that the new mode can exploit the spatial correlation better and predict the samples more precisely than the existed ones. The experimental results showed that the average gain in peak signal to noise ratio was above 0.12dB and the average reduction in bit-rate was above 1.77%, so the proposed mode is an effective prediction mode for improvement of coding performance.

关键词： AVS Interlaced video Intra prediction Video coding

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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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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Isomap and neural networks based image registration scheme

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Xu, Anbang Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)3540344373

A novel image registration scheme is proposed. In the proposed scheme, the complete isometric mapping (Isomap) is used to extract features from the image sets, and these features are input vectors of feedforward neural networks. Neural network outputs are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments for Isomap based method, the discrete cosine transform (DCT) and Zernike moment are performed. The results show that the proposed scheme is not only accurate but also remarkably robust to noise. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Self organizing maps

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