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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

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Mining for context recognition in document filtering and classification 05

Mining for context recognition in document filtering and cla...

引用

4th Annual ACIS International conference on computer and Information Science, ICIS 2005

作者： Liu, Rey-Long Dept. of Information Management Chung Hua University HsinChu Taiwan

ISBN: (纸本)0769522963

Much information has been hierarchically organized to facilitate information browsing, retrieval, and dissemination. In practice, much information may be entered at any time, but only a small subset of the information may be classified into some categories in a hierarchy. Therefore, achieving document filtering (DF) in the course of document classification (DC) is an essential basis to develop an information center, which classifies suitable documents into suitable categories, reducing information overload while facilitating information sharing. In this paper, we present a technique ICenter, which conducts DF and DC by recognizing the context of discussion (COD) of each document and category. Experiments on real-world data show that, through COD recognition, the performance of ICenter is significantly better. The results are of theoretical and practical significance. ICenter may serve as an essential basis to develop an information center for a user community, which shares and organizes a hierarchy of textual information. © 2005 ieee.

关键词： pattern recognition

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Neighborhood Preserving Projections (NPP): A novel linear dimension reduction method

Neighborhood Preserving Projections (NPP): A novel linear di...

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International conference on Intelligent Computing, ICIC 2005

作者： Pang, Yanwei Zhang, Lei Liu, Zhengkai Yu, Nenghai Li, Houqiang Information Processing Center University of Science and Technology of China Hefei 230027 China Microsoft Research Asia Beijing 100080 China

ISBN: (纸本)9783540282266

Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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A fast and memory efficient approach for fingerprint authentication system

A fast and memory efficient approach for fingerprint authent...

引用

ieee conference on Advanced Video and Signal Based Surveillance, AVSS 2005

作者： Allah, Mohamed Mostafa Abd Department of Electrical Faculty of Engineering Minia University

ISBN: (纸本)0780393856

It is still a challenge to integrate a biometrics solution such as fingerprint matching into a smart card. However, current generation of smart card is usually equipped with an 8- or 16-bit microcontroller which has limitations on hardware resources. In this paper, we present our approaches to reduce memory requirement for storing image buffer and to reduce the image processing time. Also, we show the advantages of using line extraction scheme to reduce the size of the accumulator array and to speed up the matching process. The proposed method contains more information per bit than the conventional method that can be embedded in smart cards for a real-time Match-on-Card system implementation. Furthermore, optimization in platform level offers feasibility of code integration into a low memory budget platform. An example of fingerprint identification implementation on a DSP platform with limited memory budget is presented to show effectiveness of our approach. © 2005 ieee.

关键词： pattern recognition systems

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Hidden Markov Model based 2D shape classification

Hidden Markov Model based 2D shape classification

引用

7th International conference on Advanced Concepts for Intelligent vision Systems

作者： Thakoor, N Gao, J Univ Texas Arlington TX 76013 USA Univ Texas Arlington TX 76013 USA

ISBN: (纸本)354029032X

In this paper, we propose a novel two step shape classification approach consisting of a description and a discrimination phase. In the description phase, curvature features are extracted from the shape and are utilized to build a Hidden Markov Model (HMM). The HMM provides a robust Maximum Likelihood (ML) description of the shape. In the discrimination phase, a weighted likelihood discriminant function is formulated, which weights the likelihoods of curvature at individual points of shape to minimize the classification error. The weighting scheme emulates feature selection procedure in which features important for classification are selected. A Generalized Probabilistic Descent (GPD) method based method for estimation of the weights is proposed. To demonstrate the accuracy of the proposed method, we present classification results achieved for fighter planes in terms of classification accuracy and discriminant functions.

关键词： pattern recognition

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Skin lesion segmentation using clustering techniques

Skin lesion segmentation using clustering techniques

引用

Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research society conference, FLAIRS 2005

作者： Emre Celebi, M. Guo, Wenzhao Alp Aslandogan, Y. Bergstresser, Paul R. Department of Computer Science and Engineering University of Texas at Arlington Arlington TX 76019-0015 United States Department of Dermatology University of Texas Southwestern Medical Center at Dallas Dallas TX 75390-9003 United States

ISBN: (纸本)1577352343

Cluster analysis has been widely used in various disciplines such as pattern recognition, computer vision, and data mining. In this work we investigate the applicability of two spatial clustering algorithms, namely DBSCAN and STING, to a new problem domain: Color segmentation of skin lesion (tumor) images. Automated segmentation is a key step in the computerized analysis of skin lesion images since the accuracy of the subsequent steps (feature extraction, classification, etc.) crucially depends on the accuracy of this very first step. In this paper, we develop two unsupervised methods for segmentation of skin lesion images: one based on DBSCAN clustering algorithm and the other based on STING clustering algorithm. Experiments on a database of over hundred skin lesion images show that DBSCAN-based segmentation algorithm performs significantly better than the STING-based one. Copyright © 2005, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

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Multiple cardiac arrhythmia recognition using adaptive wavelet network

Multiple cardiac arrhythmia recognition using adaptive wavel...

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2005 27th Annual International conference of the Engineering in Medicine and Biology society, ieee-EMBS 2005

作者： Lin, Chia-Hung Chen, Pei-Jarn Chen, Yung-Fu Lee, You-Yun Chen, Tainsong Department of Electrical Engineering Kao-Yuan Institute of Technology Kaohsiung 821 Taiwan Institute of Biomedical Engineering National Cheng-Kung University Tainan 701 Taiwan Department of Computer Science and Information Engineering Dayeh University Changhua 515 Taiwan Department of Electrical Engineering Southern Taiwan University of Technology Tainan Conty 710 Taiwan

ISBN: (纸本)0780387406

This paper proposes a method for electrocardiogram (ECG) heartbeat pattern recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting from feature extraction and conversion of QRS complexes, and then identifying cardiac arrhythmias based on the detected features. The discrimination method of ECG beats is a two-subnetwork architecture, consisting of a wavelet layer and a probabilistic neural network (PNN), Morlet wavelets are used to extract the features from each heartbeat, and then PNN is used to analyze the meaningful features and perform discrimination tasks. The AWN is suitable for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results obtained by testing the data of the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed method. © 2005 ieee.

关键词： Electrocardiography

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Exploratory investigations for intelligent damage prognosis using hidden markov models

Exploratory investigations for intelligent damage prognosis ...

引用

ieee Systems, Man and Cybernetics society, Proceedings - 2005 International conference on Systems, Man and Cybernetics

作者： Rammohan, Roshan Taha, Mahmoud Reda Department of Computer Science University of New Mexico Albuquerque NM - 87131 United States Department of Civil Engineering University of New Mexico Albuquerque NM - 87131 United States

Structural damage occurs due to structural overloading or due to environmental conditions or combined effects. Extensive research on structural health monitoring, damage diagnosis and damage pattern recognition have been developed over the last two decades. Recently, damage prognosis has become a new research focus that tries to use damage diagnosis knowledge to inform the responsible authorities on the expected remaining service life of the structure such that efficient maintenance operations can be scheduled. We demonstrate here the use of hidden Markov Models to model damage accumulation and propagation in structures and to predict the residual service life of the structure given damage diagnosis observations. The proposed model is described and is tested using damage diagnosis simulated data of a pre-stressed concrete bridge. ©2005 ieee.

关键词： Artificial intelligence

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Nonsingular discriminant feature extraction for face recognition

Nonsingular discriminant feature extraction for face recogni...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Liao, Chih-Pin Chien, Jen-Tzung Department of Computer Science and Information Engineering National Cheng Kung University Tainan 70101 Taiwan

ISBN: (纸本)0780388747

It is popular to extract discriminant features using Fisher linear discriminant analysis (LDA) for general pattern recognition. LDA aims to find an optimal discriminant transformation matrix, which maximizes the ratio of between-class scatter to within-class scatter. However, in case of small sample size and high dimensional data, LDA is prone to be unrealizable due to the singularity of scatter matrices. In this paper, we present a nonsingular transformation prior to performing LDA. This method is to transform general features using all eigenvectors of scatter matrix with nonzero eigenvalues. As a result, the scatter matrix of transformed features is nonsingular. Subsequently, the discriminant transformation is applied according to LDA using the new scatter matrices. The superiority of nonsingular discriminant analysis of between-class matrix comes from the shrinkage of within-class scatters and accordingly the enhancement of Fisher class separability. From the experiments on facial databases, we find that the nonsingular discriminant feature extraction achieves significant face recognition performance compared to other LDA-related methods for a wide range of sample sizes and class numbers. © 2005 ieee.

关键词： Feature extraction

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Visual hand tracking using nonparametric sequential belief propagation

Visual hand tracking using nonparametric sequential belief p...

引用

International conference on Intelligent Computing, ICIC 2005

作者： Liang, Wei Jia, Yunde Ge, Cheng Department of Computer Science and Engineering Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9783540282266

Hand tracking is a challenging problem due to the complexity of searching in a 20+ degrees of freedom space for an optimal estimate. This paper develops a statistical method for robust visual hand tracking, in which graphical model decoupling different hand joints is performed to represent the hand constraints. Each node of the graphical model represents the position and the orientation of each hand joint in world coordinate. Then, the problem of hand tracking is transformed into an inference of graphical model. We extend Nonparametric Belief Propagation to a sequential process to track hand motion. The Experiment results show that this approach is robust for 3D hand motion tracking. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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The spectrum enhancement algorithm for feature extraction and pattern recognition

The spectrum enhancement algorithm for feature extraction an...

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Intelligent Robots and computer vision XXIII: Algorithms, Techniques, and Active vision

作者： Crosta, Giovanni Franco Inverse Problems and Mathematical Morphology Unit Department of Environmental Sciences Università Degli Studi Milano - Bicocca 1 Piazza delia Scienza I-20126 Milan Italy

The "enhanced spectrum" of an image g[&middot] is a fonction h[&middot] of wavenumber u obtained as follows. A reflection operation Q[&middot] is applied to g[&middot];the power spectral dens... 详细信息

ISBN: (纸本)0819460303

The "enhanced spectrum" of an image g[·] is a fonction h[·] of wavenumber u obtained as follows. A reflection operation Q[·] is applied to g[·];the power spectral density | G[u]| 2 of Q[g[·]] is converted to the Log scale and averaged over a suitable arc;the function s[·] of u alone is thus obtained, from which a known function, the "model" m[u], is subtracted: this yields h[u]· Models m(p)[·] used herewith have a roll-off like -10Log10[up]. As a consequence spectrum enhancement is a non-linear image filter which is shown to include partial spatial differentiation of Q[g[·]] of suitable order. The function h[·] emphasizes deviations of s[·] from the prescribed behaviour m (P)W[·]. The enhanced spectrum is used herewith as the morphological descriptor of the image after polynomial interpolation. Multivariate statistical analysis of enhanced spectra by means of principal components analysis is applied with the objective, of maximizing discrimination between classes of images. Recent applications to materials science, cell biology and environmental monitoring are reviewed.

关键词： Image enhancement

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