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Efficient multi-resolution detection of binary object

Information Technology Journal 2010年第8期9卷 1641-1646页

作者： Fang, Xianyong Wu, Hao MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Anhui 230039 China

This study presents an efficient multi-resolution method to detect binary object directly. Both intensity and geometry differences are used to measure the similarity between the source object template and target objects inside the test image. Especially for the geometrical difference, the histogram of oriented gradients is proposed to measure the similarity of the edge images between the source object template and the target object. Based on these two measurements, a multi-resolution strategy is used to accurately locate the object position in a coarse-to-fine way, where intensity difference and geometry difference applied consequently in each layer. Experimental results demonstrate the efficiency of the proposed method. © 2010 Asian Network for Scientific Information.

关键词： Geometry

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Manifold feature extraction for video based on locally linear embedding

Manifold feature extraction for video based on locally linea...

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International Conference on Computer Science and Education

作者： Fu, Maosheng Xu, Qin Kong, Min Luo, Bin Key Lab. of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Computer Science and Technology West Anhui University Anhui Lu'an 237012 China

ISBN: (纸本)9781424460052

Locally linear embedding (LLE) is an elegant nonlinear method for feature extraction and manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. However, LLE algorithm fails when it is directly applied to video with multi-shot. In this paper, video manifold feature is defined firstly, and then using LLE we extract video manifold feature through adding virtual frames on an enriched set. The proposed manifold feature extraction method is applied to shot transition and video trajectories. The video manifold feature description gives a new tool for video analysis. ©2010 IEEE.

关键词： Feature extraction

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Motor Imagery EEG Classification based on Dynamic ICA Mixing Matrix

Motor Imagery EEG Classification based on Dynamic ICA Mixing...

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The 4th International Conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Xiaojing Guo Xiaopei Wu The Key Laboratory of Intelligent Computing & Signal Processing of MOE Anhui University Hefei city Anhui province China

Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. To motor-imagery-based BCI, feature extraction and classification are two critical stages. This paper explores a dynamic ICA base on sliding window Infomax algorithm to analyze motor imagery EEG. The method can get a dynamic mixing matrix with the new data inputting, which is unlike the static mixing matrix in traditional ICA algorithm. And by using the feature patterns based on total energy of dynamic mixing matrix coefficients in a certain time window, the classification accuracy without training can be achieved beyond 85% for BCI competition 2003 data set Ⅲ. The results demonstrate that the method can be used for the extraction and classification of motor imagery EEG. In the present study, it suggests that the proposed algorithm may provide a valuable alternative to study motor imagery EEG for BCI applications.

关键词： Electroencephalography Independent component analysis signal processing algorithms Brain computer interfaces Computer interfaces Rhythm laboratories signal processing Cities and towns Feature extraction

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Speech Endpoint Detection in Noisy Environments Using EMD and Teager Energy Operator

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Journal of Electronic Science and Technology 2010年第2期8卷 183-186页

作者： De-Xiang Zhang Xiao-Pei Wu Zhao Lv Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

关键词： Index Terms----Empirical mode decomposition endpoint detection noisy speech Teager energy operator.

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SPEECH ENDPOINT DETECTION IN NOISY ENVIRONMENTS USING EMD AND TEAGER ENERGY OPERATOR

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Journal of Electronic Science and Technology 2010年第2期000卷 183-186页

作者： DE-XIANG ZHANG XIAO-PEI WU ZHAO Lü Key Lab.of Intelligent Computing and Signal Processing of Ministry of Education.Anhui University Hefei 230039China

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition (EMD) algorithm and Teager energy operator (TEO) is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

关键词： Empirical mode decomposition endpoint detection noisy speech Teager energy operator

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Face recognition using fuzzy rough set and support vector machine

Face recognition using fuzzy rough set and support vector ma...

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IEEE International Conference on intelligent computing and intelligent Systems (ICIS)

作者： Shi-yi Wang Tao Liang Computer Department of Chaohu College Chaohu P.R. China MOE Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei Anhui P.R. China MOE Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei 230039 China

This paper proposes a method of face recognition using the support vector machine (SVM) based on the fuzzy rough set theory (FRST). Firstly, features from human face images are extracted by combining the 2-D wavelet decomposition technique with the grayscale integral projection technique. And then, the attribute reduction algorithm based on FRST is applied in face recognition. The reduction algorithm based on FRST can eliminate the redundant features of sample dataset and reduce the space dimension of the sample data. The proposed method avoids losing of information caused by dispersing before original rough set attribute reduction. Experimental results show that it can improve the classification accuracy in face recognition as compared with the method using the original rough set.

关键词： Face

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GPU-Based Accelerated FDTD Simulations for Double Negative (DNG) Materials Applications

GPU-Based Accelerated FDTD Simulations for Double Negative (...

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2010 International Conference on Microwave and Millimeter Wave Technology(2010国际微波与毫米波技术会议 ICMMT2010)

作者： Shen Chen Sun Dong Wu Xian-liang Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei 230039 china

Recently, double-negative meta-materials are widely studied in scientific research. The double-negative (DNG) mediums are characterized by simultaneous negative permittivity and permeability. In order to make the FDTD method analyze the electromagnetic scattering and propagation for double-negative (DNG) medium, z-transform is applied to the FDTD method in the double-negative (DNG) medium. For the simulations, extremely large computer memory space and a long computational time b required. A parallel algorithm for the FDTD method on the state of the art graphics hardware is presented. The parallel computing techniques can be used to reduce the computation time significantly and have been widely applied in various complex FDTD applications. In this paper, we simulate the interaction between electromagnetic wave and DNG medium, and describe an impact of new GPU features on development process of an efficient Finite Difference Time Domain (FDTD) implementation.

关键词： Acceleration Finite difference methods Time domain analysis Electromagnetic scattering Computational modeling Permittivity Permeability Electromagnetic analysis Electromagnetic propagation Computer simulation

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On dynamic weighting of data in clustering with K-alpha means

On dynamic weighting of data in clustering with K-alpha mean...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab. of Child Development and Learning Science Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9780769541099

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances. © 2010 IEEE.

关键词： Clustering algorithms

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781424459346

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD. ©2010 IEEE.

关键词： Discriminant analysis

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Application of AWE to scattering analysis of the double-negative medium (RCS) within a wider band.

Application of AWE to scattering analysis of the double-nega...

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第九届国际天线、电波传播及电磁理论学术研讨会(ISAPE2010)

作者： Mei Yang Yu-Xin Sun Hui Wang Ming-Sheng Chen Xian-liang Wu Key Lab of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei Department of Physics and Electronic Engineering Hefei Normal University Hefei China School of In Key Lab of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei

The PMCHWT equation of the double negative media (DNG) is obtained based on its constitutive relationship. And the surface currents and radar cross section (RCS) at a single frequency point is computed by Method of moments (MOM). The asymptotic waveform evaluation (AWE) technique for dispersive dielectric medium is deduced and apllied to electromagnetic scattering analysis of double-negative medium within a given frequency band.

关键词： Moment methods Optical propagation Silicon compounds

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