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检索条件"机构=Key Lab. of Intelligent Computing & Signal Processing"

共 446 条记录，以下是331-340 订阅

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A sample pre-mapping method enhancing boosting for object detection

A sample pre-mapping method enhancing boosting for object de...

引用

International Conference on Pattern Recognition

作者： Ren, Haoyu Hong, Xiaopeng Heng, Cher-Keng Liang, Luhong Chen, Xilin Key Lab. of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology CAS China School of Computer Science and Technology Harbin Institute of Technology Panasonic Singapore Laboratories Pte Ltd.

ISBN: (纸本)9780769541099

We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection. The key step of the proposed method is a sample pre-mapping on original space by referring to the selected 'reference sample' before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating hyper-plane in an implicit high-dimensional space, so that the resulting classifier could achieve the performance similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show that the proposed approach achieves performance comparable with the common-used methods on public datasets in both pedestrian detection and car detection. © 2010 IEEE.

关键词： Object detection

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Boosted sigma set for pedestrian detection

Boosted sigma set for pedestrian detection

引用

International Conference on Pattern Recognition

作者： Hong, Xiaopeng Chang, Hong Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology China Key Lab. of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China Institute of Digital Media Peking University China

ISBN: (纸本)9780769541099

This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set. Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discriminative power more efficiently because the classification on Sigma sets can be directly performed in vector space. Experimental results on the INRIA and the Daimler Chrysler pedestrian datasets show the effectiveness and efficiency of the proposed method. © 2010 IEEE.

关键词： Covariance matrix

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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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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, lab.led data are very limited while unlab.led data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate lab.led data. To take advantage of unlab.led 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...

引用

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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Compressive Sensing for Wide-Angle Analysis

Compressive Sensing for Wide-Angle Analysis

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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： Hong-Mei Du Yi-Cai Mei Si-Long Lu 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 230601 China Schoo Key Lab of Intelligent Computing & Signal Processing Anhui University Ministry of Education Hefei

A new incident source with different angles was constructed for dealing with wide-angle scattering problems. Considering the impendence matrix in method of moments (MOM） is independent from incident angles, the equivalent relationship between induced current and the measured CS-current was build, while the CS-current can be computed directly under the new incident source. Finally, we can reconstruct the induce current by utilizing the theory of compressive sensing (CS). Compared with traditional MOM, the computational complexity can be greatly reduced.

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Efficient incremental decoding for tree-to-string translation

Efficient incremental decoding for tree-to-string translatio...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2010

作者： Huang, Liang Mi, Haitao Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey CA 90292 United States Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)1932432868

Syntax-based translation models should in principle be efficient with polynomially-sized search space, but in practice they are often embarassingly slow, partly due to the cost of language model integration. In this paper we borrow from phrase-based decoding the idea to generate a translation incrementally left-to-right, and show that for tree-to-string models, with a clever encoding of derivation history, this method runs in average-case polynomial-time in theory, and linear-time with beam search in practice (whereas phrase-based decoding is exponential-time in theory and quadratic-time in practice). Experiments show that, with comparable translation quality, our tree-to-string system (in Python) can run more than 30 times faster than the phrase-based system Moses (in C++). © 2010 Association for Computational Linguistics.

关键词： Decoding

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Numerical Simulation of GPR based on 3-D High-order FDTD

Numerical Simulation of GPR based on 3-D High-order FDTD

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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： Zhi-xiang Huang Hong-mei Du Yi-cai Mei Xin-gang Ren 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

The high-order finite-difference time-domain (HO-FDTD) technique is used in the simulation of ground-penetrating radar modeling in three dimensions (3-D), which can improve accuracy and reduce the error caused by numerical dispersion effectively. To absorb waves reflected from edges we implement convolutional perfectly matched layer (CPML) absorbing boundaries. It can efficiently absorb the reflections and greatly increase the computation efficiency. The surface-based reflection and cross-hole GPR modeling are simulated, and numerical results show the efficiency of the method.

关键词： Materials

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A Network-based Approach for Protein Functions Prediction Using Locally Linear Embedding

A Network-based Approach for Protein Functions Prediction Us...

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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： Haifeng Zhao Dengdi Sun Rifeng Wang Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 P.R.China

Inferring protein functions from different data sources is a challenging task in the post-genomic era, as a large number of crude protein structures from structural genomics project are now solved without their biochemical functions characterized. Recently, many different methods have been used to predict protein functions including those based on Protein-Protein Interaction (PPI), structure, sequence relationship, gene expression data, etc. Among these approaches, methods based on protein interaction data are very promising. In this paper, we studied a network-based method using locally linear embedding (LLE). LLE is a robust learning algorithm that manipulates dimensionality reduction, neighborhood-preserving embedding for high-dimensional data. We first embed both annotated and unannotated proteins in a low dimensional Euclidean space;then, we apply semi-supervised learning techniques to classify unannotated proteins into different functional groups. Finally, we made predictions to the unknown functional proteins in yeast. 5-fold cross validation is then applied to the GO terms to compare the performance of different approaches, and the proposed method performs significantly better than the others.

关键词： Bioinformatics Semisupervised learning Genomics Protein sequence Gene expression Learning systems Sun intelligent networks intelligent structures Computer networks

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Inferring Protein Annotation from Topological Structural Analysis on Protein Interaction Network

Inferring Protein Annotation from Topological Structural Ana...

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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： Dengdi Sun Jin Fan Haifeng Zhao Bin Luo Key Lab of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 P.R.China

As more and more high-throughput protein-protein interactions data are collected, a large fraction of newly discovered proteins have an unknown functional role. A challenge to the scientific community is to assign these newly proteins with a biological function that can be verified by experiment. On the basis of thorough analysis of existing protein function prediction, we take double direction enumeration method combined with the distance constrains to find protein pathway, and propose a new predicting model based on topological structural of protein-protein interaction (PPI) network and protein pathway, which greatly increases the speed of the algorithm. To validate the method, the Yeast Saccharomyces cerevisiae protein-protein interaction network and corresponding protein pathways are analyzed. Comparing with other methods, our results can substantially improve the accuracy and robustness of functional annotation.

关键词： Proteins Fungi Databases Computer networks Biological system modeling Predictive models Organisms signal analysis Sun intelligent networks

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