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International Conference on Pattern Recognition

作者： Haisen Li Yanning Zhang Haichao Zhang Yu Zhu Jinqiu Sun Shaanxi Key Laboratory of Speech and Image Information Processing School of Computer Science and Technology Northwestern Polytechnical University China Institute of Precision Guidance and Control School of Astronautics Northwestern Polytechnical University China

ISBN: (纸本)9781467322164

Blind image deblurring, aiming at obtaining the sharp image from blurred one, is a widely existing problem in image processing. Traditional image deblurring methods always use the deconvolution method to remove the blur kernel's effect, however, deconvolution is so sensitive to noise that inevitable artifacts always exist in the deblurring results, even though regularity terms are introduced as constraints. In this paper, we propose a novel blind image deblurring method based on the sparse prior of dictionary pair, estimating the sparse coefficient, sharp image and blur kernel alternately. The proposed method could avoid the deconvolution problem which is an ill-posed problem, and obtain the result with fewer artifacts. Compared with the state-of-the-art method, experimental results demonstrate that the proposed method could obtain better performance.

关键词： Dictionaries Image restoration Kernel Deconvolution Mathematical model Noise Image resolution

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Hyperspectral image classification using band selection and morphological profile

Hyperspectral image classification using band selection and ...

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Workshop on Hyperspectral Image and Signal processing: Evolution in Remote Sensing, WHISPERS

作者： Kun Tan Erzhu Li Qian Du Peijun Du Jiangsu Key laboratory of Resources and Environment Information Engineering China University of Mining and Technology China Department of Electrical and Computer Engineering Mississippi State University USA Department of Geographical Information Science Nanjing University China

In this paper, we propose a new methodology to combine spectral information and spatial features for Support Vector Machine (SVM)-based classification. The novelty of the proposed work is in the combination of band selection (i.e., linear prediction (LP)-based method), spatial feature extraction (i.e., morphology profiles (MP)), and spectral transformation (i.e., principal component analysis (PCA)) to build a computationally tractable system. The preliminary result with ROSIS data shows that using the selected bands and MP features extracted from principal components (PCs) can yield the highest accuracy. We believe such finding is instructive to feature extraction/selection for spectral/spatial-based hyperspectral image classification.

关键词： Abstracts Indexes Accuracy Statistical learning Hyperspectral imaging

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STRUCTURED SPARSE LINEAR DISCRIMINANT ANALYSIS

STRUCTURED SPARSE LINEAR DISCRIMINANT ANALYSIS

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IEEE International Conference on Image processing

作者： Zhen Cui Shiguang Shan Haihong Zhang Shihong Lao Xilin Chen School of Computer Science and Technology Huaqiao University Xiamen China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Omron Social Solutions Company Limited Kyoto Japan

Linear Discriminant Analysis (LDA) is an efficient image feature extraction technique by supervised dimensionality reduction. In this paper, we extend LDA to Structured Sparse LDA (SSLDA), where the projecting vectors are not only constrained to sparsity but also structured with a pre-specified set of shapes. While the sparse priors deal with small sample size problem, the proposed structure regularization can also encode higher-order information with better interpretability. We also propose a simple and efficient optimization algorithm to solve the proposed optimization problem. Experiments on face images show the benefits of the proposed structured sparse LDA on both classification accuracy and interpretability.

关键词： Face Face recognition Databases Linear discriminant analysis Optimization Training Algorithm design and analysis

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Sentence alignment for web page text based on vector space model

Sentence alignment for web page text based on vector space m...

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International Conference on computer Science and information processing (CSIP)

作者： GuanHong Zhang Odbal Department of Computer science and technology Key Laboratory of Network and Intelligent Information Processing Hefei University Hefei Anhui China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei Anhui China

There exist noisy, unparallel sentences in parallel web pages. Web page structure is subjected to some limitation for sentences alignment task for web page text. The most straightforward way of aligning sentences is using a translation lexicon. However, a major obstacle to this approach is the lack of dictionary for training. This paper presents a method for automatically align Mongolian-Chinese parallel text on the Web via vector space model. Vector space model is an algebraic model for representing any object as vectors of identifiers, such as index terms. In the statistically based vector-space model, a sentence is conceptually represented by a vector of keywords extracted from the text. Extracted keywords are composed by content words, known as terms and the weight of a term in a sentence vector can be determined tf-idf method. CHI is used to compute the association between bilingual words. Once the term weights are determined, the similarity between sentence vectors is computed via cosine measure. The experimental results indicate that the method is accurate and efficient enough to apply without human intervention.

关键词： Manganese HTML

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Reconstructing 3D human poses from uncalibrated monocular images with strong perspective effects

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Journal of Computational information Systems 2012年第15期8卷 6437-6445页

作者： Chen, Shu Peng, Xiaoning School of Information Engineering Xiangtan Universtiy Xiangtan 411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan 411105 China School of Information Science and Engineering Central South University Changsha 410083 China Department of Computer Science and Technology Huaihua College Huaihua 418008 China

This paper present a geometric method to reconstruct human motion pose from 2D point correspondences obtained from uncalibrated monocular images. The proposed algorithm can handle images with very strong perspective effects, which is guaranteed by the fact that only one scale factor corresponding to the root joint in the skeleton model is required estimating. To take fully advantage of this fact, a system of two nonlinear equations with two variables to estimate the scale factor of the root joint is established, and the scale factor of the root joint is optimized by the Gauss-Newton iterative scheme. At last, a framework to reconstruct human motion pose is presented. Quantitive analysis of the experiment results based on several real monocular video sequences shows the accuracy of the scale factor as well as the accuracy of the pose reconstruction. © 2012 Binary information Press.

关键词： Nonlinear equations

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A novel granular support vector machine based on mixed kernel function

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第20期6卷 484-492页

作者： Huang, Huajuan Ding, Shifei Jin, Fengxiang Yu, Junzhao Han, Youzhen School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.

关键词： Support vector machines

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Twin support vector machines based on rough sets

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第20期6卷 493-500页

作者： Yu, Junzhao Ding, Shifei Jin, Fengxiang Huang, Huajuan Han, Youzhen School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100190 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

TWSVM(Twin Support Vector Machines) is based on the idea of GEPSVM (Proximal SVM based on Generalized Eigenvalues), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. In order to further improve the speed and accuracy of TWSVM, this paper proposes the twin support vector machines based on rough sets. Firstly, using the rough sets theory to reduce the attributes, and then using TWSVM to train and predict the new datasets. The final experimental results and data analysis show that the proposed algorithm has higher accuracy and better efficiency compared with the traditional twin support vector machines.

关键词： Rough set theory

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A Centroid location algorithm based on Furthermost Beacon with application to wireless sensor networks

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International Journal of Wireless and Mobile Computing 2012年第3期5卷 259-262页

作者： Chen, Wei Wang, Qian Wang, Xin Tian, Qi-Chong College of Computer Science and Technology China University of Mining and Technology Xuzhou Jiangsu 221116 China State Key Laboratory of Coal Resources and Safe Mining China University of Mining and Technology Xuzhou Jiangsu 221116 China Information Science and Engineering Department Southeast University Nanjing Jiangsu 210096 China Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China

Simple and efficient location algorithms are of great research significance to Wireless Sensor Networks (WSNs) systems. In this paper, main factors of position error in the Centroid location algorithm are analysed, and then a Furthermost Beacon (FB) location algorithm is presented. In the range of the communication radius of the nodes to be located, the algorithm estimates four beacon nodes or more with highest distribution degree around the unknown nodes. A group of experiments have been conducted and the results of the experiments have demonstrated the superior performance of the proposed algorithm. Copyright © 2012 Inderscience Enterprises Ltd.

关键词： Sensor nodes

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Hybrid neural network model application in annual precipitation forecast

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International Journal of u- and e- Service, Science and technology 2012年第4期5卷 21-30页

作者： Ma, Li Li, Xuelian Wang, Jin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science and Technology Nanjing 210044 China

When applied to precipitation forecasting, the mean generating function - optimal subset regression (MGF-OSR) model is limited by its low accuracy and high error, while the back propagation (BP) neural network model has difficulty in learning for matrix selection. This paper proposes a new MGF-OSR-BP model, which uses a MGF to extend original data, an OSR to select the best series as the BP neural network input node and learning matrix, and the resultant data for training. The training procedure determines the number of hidden layers and uses an optimal number of hidden layers for model training. This paper uses the MGF-OSR-BP model to analyze precipitation data from Hangzhou, China, for 53 years, from 1956 to 2008. The 1956-2006 precipitation data are used as the training sample, and the 2007-2008 data are used as the test set data to verify the practicality of the forecast system. A fitting verification is performed using the forecasted data against field measurement data, and the results show that the forecast accuracy is better than that of the MGF-OSR model or the MGF stepwise multiple regression model.

关键词： Backpropagation

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Image sets alignment for Video-Based Face Recognition

Image sets alignment for Video-Based Face Recognition

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhen Cui Shiguang Shan Haihong Zhang Shihong Lao Xilin Chen School of Computer Science and Technology Huaqiao University Xiamen China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Omron Social Solutions Company Limited Kyoto Japan

Video-based Face Recognition (VFR) can be converted to the matching of two image sets containing face images captured from each video. For this purpose, we propose to bridge the two sets with a reference image set that is well-defined and pre-structured to a number of local models offline. In other words, given two image sets, as long as each of them is aligned to the reference set, they are mutually aligned and well structured. Therefore, the similarity between them can be computed by comparing only the corresponded local models rather than considering all the pairs. To align an image set with the reference set, we further formulate the problem as a quadratic programming. It integrates three constrains to guarantee robust alignment, including appearance matching cost term exploiting principal angles, geometric structure consistency using affine invariant reconstruction weights, smoothness constraint preserving local neighborhood relationship. Extensive experimental evaluations are performed on three databases: Honda, MoBo and YouTube. Compared with competing methods, our approach can consistently achieve better results.

关键词： Manifolds Face Image matching Face recognition Image reconstruction Video sequences Computational modeling

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