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international Conference on computer Modeling and Simulation, UKSIM

作者： Patrick S P Wang Northeastern University Boston MA US

This talk deals with fundamental aspects of intelligent pattern Recognition (IPR) and applications. It basically includes the following: Overview of 3D Biometric Technology and Applications, Importance of Security: A Scenario of Terrorists Attack, What are Biometric Technologies? Biometrics: analysis vs synthesis, analysis: Interactive pattern Recognition Concept, Importance of Measurement, How it works: Fingerprint Extraction and Matching, Iris, and Facial analysis, Authentication Applications, Thermal Imaging: Emotion Recognition. synthesis in Biometrics, Modelling and Simulation, and more Examples and Applications of 3D Biomedical Imaging and Vision in Interactive Web/Video Networking Fuzzy e-Learning Environment. Finally, some future research directions are discussed.

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Face Recognition Based on Principle Component analysis and Support Vector Machine

Face Recognition Based on Principle Component Analysis and S...

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international workshop on intelligent Systems and Applications, ISA

作者： Chengliang Wang Libin Lan Yuwei Zhang Minjie Gu Department of Computer Science and Engineering Chongqing University Chongqing China

Face recognition is an important research field of pattern *** to now,it caused researchers great concern from these fields,such as pattern recognition,computer vision,and physiology,and so *** recognition algorithms have been proposed. Generally,we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify them into a class ***,it is necessary for us to pay close attention to feature extractor and *** this paper, in order to raise recognition rate,Principle Component analysis (PCA) is used to extract image feature,and Support Vector Machine (SVM) is used to deal with face recognition problem. SVM has been recently proposed as a new classifier for pattern *** take Principle Component analysis & Support Vector Machine (PCA&SVM) to do experiments on the Cambridge ORL Face database,and compare this method with Principle Component analysis & Nearest Neighbor (PCA&NN) and Support Vector Machine (SVM) on recognition rate and recognition time ***,this experimental results show that recognition rate of this method,under small samples circumstance,is better than other two methods. It shows that,for face recognition,sending PCA features to SVM classifiers is feasible and correct.

关键词： Support vector machines Face Face recognition Training Feature extraction Principal component analysis

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Parallel positive Boolean function approach to classification of remote sensing images

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Journal of Applied Remote Sensing 2011年第1期5卷

作者： Chang, Yang-Lang Hsieh, Tung-Ju Plaza, Antonio Chen, Yen-Lin Liang, Wen-Yew Fang, Jyh-Perng Huang, Bormin National Taipei University of Technology Department of Electrical Engineering No. 1 Chung-Hsiao E. Road Taipei 10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering No. 1 Chung-Hsiao E. Road Taipei 10608 Taiwan University of Extremadura Department of Technology of Computers and Communications Escuela Politecnica 10003 Caceres Spain University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies Space Science and Engineering Center Madison WI 53706 United States

We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification. © 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Message passing

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Keynote Speech 1: intelligent pattern Recognition and Applications——Modeling and Simulation in HC Interactive Environment

Keynote Speech 1: Intelligent Pattern Recognition and Applic...

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2011 international Conference on Physical Education and Society Management (ICPESM2011)

作者： Patrick S.P.Wang ISIBM and WASE IEEE and ISIBM Computer and Information Science Northeastern University ECNU University of Calgary University Magdeburg

This talk deals with fundamental aspects of intelligent pattern Recognition(IPR) and applications. It basically includes the following: Overview of 3D Biometric Technology and Applications, Importance of Security: A Scenario of Terrorists Attack,, What are Biometric Technologies? Biometrics: analysis vs synthesis, analysis: Interactive pattern Recognition Concept, Importance of Measurement, How it works: Fingerprint Extraction and Matching, Iris, and Facial analysis, Authentication Applications, Thermal Imaging: Emotion Recognition. synthesis in Biometrics, Modeling and Simulation, and more Examples and Applications of 3D Biomedical Imaging in Interactive Web/Video Networking Fuzzy Learning Environment. Finally, some future research directions are discussed.

关键词： Simulation intelligent pattern Recognition and Applications Keynote Speech 1 Modeling and Simulation in HC Interactive Environment

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A novel rules extraction method based on clustering analysis

A novel rules extraction method based on clustering analysis

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2nd international workshop on intelligent Systems and Applications, ISA2010

作者： Tang, Zhi-Hang Peng, Hui-Ying School of Computer and Communication Hunan Institute of Engineering Xiangtan 411104 China College of Business Administer South China University of Technology Guangzhou 510640 China

ISBN: (纸本)9781424458745

Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformaticsa novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by discrete activation values of hidden units. According to discrete activation values of this hidden unit, cluster weights from input units to it. The incremental rules are extracted and the existing rule set is updated based on this algorithm. The result shows this method is quite valuable. ©2010 IEEE.

关键词： Neural networks

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Identification of Co-occurring Insertions in Cancer Genomes Using Association analysis

Identification of Co-occurring Insertions in Cancer Genomes ...

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IEEE international Conference on Bioinformatics and Biomedicine workshops (BIBMW)

作者： Steinbach, Michael Yu, Haoyu Kumar, Vipin Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Minn Supercomp Inst Minneapolis MN USA

ISBN: (纸本)9781424483044

Collections of tumor genomes created by insertional mutagenesis experiments, e.g., the Retroviral Tagged Cancer Gene Database, can be analyzed to find connections between mutations of specific genes and cancer. Such connections are found by identifying the locations of insertions or groups of insertions that frequently occur in the collection of tumor genomes. Recent work has employed a kernel density approach to find such commonly occurring insertions or co- occurring pairs of insertions. Unfortunately, this approach is extremely compute intensive for pairs of insertions, and even more intractable for triples, etc. We present a novel approach that combines kernel density and association analysis (frequent pattern mining) techniques to efficiently find commonly co- occurring sets of insertions of any length. More generally, this approach can be used to find other commonly occurring features in collections of genomes.

关键词： component frequent pattern mining cancer genomes kernel density

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Artificial Neural Networks in pattern Recognition - 4th IAPR TC3 workshop, ANNPR 2010, Proceedings

Artificial Neural Networks in Pattern Recognition - 4th IAPR...

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4th IAPR TC3 workshop on Artificial Neural Networks in pattern Recognition, ANNPR 2010

ISBN: (纸本)3642121586

The proceedings contain 24 papers. The topics discussed include: pattern classification using a penalized likelihood method;evaluation of feature selection by multiclass kernel discriminant analysis;correlation-based and causal feature selection analysis for ensemble classifiers;a new Monte Carlo-based error rate estimator;recognition of sequences of graphical patterns;maximum echo-state-likelihood networks for emotion recognition;robustness analysis of eleven linear classifiers in extremely high-dimensional feature spaces;global coordination based on matrix neural gas for dynamic texture synthesis;sic-means: a semi-fuzzy approach for clustering data streams using C-means;the mathematics of divergence based online learning in vector quantization;cluster analysis of cortical pyramidal neurons using SOM;neural network cascade for facial feature localization;and a hidden Markov model based approach for facial expression recognition in image sequences.

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Document analysis applied to fragments: Feature set for the reconstruction of torn documents 10

Document analysis applied to fragments: Feature set for the ...

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2010 IAPR workshop on Document analysis Systems, DAS 2010

作者： Diem, Markus Kleber, Florian Sablatnig, Robert Institute of Computer Aided Automation Favoritenstr. 9/1832 1040 Vienna Austria

ISBN: (纸本)9781605587738

Document analysis is done to analyze entire forms (e.g. intelligent form analysis, table detection) or to describe the layout/structure of a document. In this paper document analysis is applied to snippets of torn documents to calculate features that can be used for reconstruction. The main intention is to handle snippets of varying size and different contents (e.g. handwritten or printed text). Documents can either be destroyed by the intention to make the printed content unavailable (e.g. business crime) or due to time induced degeneration of ancient documents (e.g. bad storage conditions). Current reconstruction methods for manually torn documents deal with the shape, or e.g. inpainting and texture synthesis techniques. In this paper the potential of document analysis techniques of snippets to support a reconstruction algorithm by considering additional features is shown. This implies a rotational analysis, a color analysis, a line detection, a paper type analysis (checked, lined, blank) and a classification of the text (printed or hand written). Preliminary results show that these features can be determined reliably on a real dataset consisting of 690 snippets. Copyright 2010 ACM.

关键词： Text processing

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License Plate Character Recognition Based on Gaussian-Hermite Moments

License Plate Character Recognition Based on Gaussian-Hermit...

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international workshop on Education Technology and computer Science, ETCS

作者： Xiaojuan Ma Renlong Pan Lin Wang Guizhou Key Laboratory of Pattern Recognition & Intelligent Control Guizhou University for Nationalities Guiyang China

ISBN: (纸本)9781424463886;9780769539874

Character recognition plays an important role in the automatic license plate recognition (ALPR) system. In this paper, we propose a new method to recognize the license plates characters by using 2D Gaussian-Hermite moments (GHMs) of different orders with 231 GHMs features as the input vector of BP neural network. The system worked under variable illumination, variable size of plate and dynamic backgrounds. The experimental results demonstrate robust and efficient of our method.

关键词： Licenses Character recognition Gaussian processes Neural networks pattern recognition Image segmentation intelligent transportation systems Robustness Image recognition pattern analysis

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A Novel Rules Extraction Method Based on Clustering analysis

A Novel Rules Extraction Method Based on Clustering Analysis

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international workshop on intelligent Systems and Applications, ISA

作者： Zhi-hang Tang Hui-ying Peng School of Computer and Communication Hunan Institute of Engineering Xiangtan China College of Business Administer South China University of Technology Guangzhou China

ISBN: (纸本)9781424458721;9781424458745

Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by discrete activation values of hidden units. According to discrete activation values of this hidden unit, cluster weights from input units to it. The incremental rules are extracted and the existing rule set is updated based on this algorithm. The result shows this method is quite valuable.

关键词： Data mining Clustering algorithms Neural networks Unsupervised learning Data analysis Machine learning pattern recognition Image analysis Bioinformatics Machine learning algorithms

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