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Adaptive wavelets for image representation and classification

Adaptive wavelets for image representation and classificatio...

引用

European Signal Processing conference (EUSIPCO)

作者： Gemma Piella Marine Campedel Béatrice Pesquet-Popescu GET-ENST Signal and Image Processing Department Paris France

In this paper we propose a feature-based wavelet representation for image classification and visualization. The work is primarily motivated by the need to classify quickly and efficiently large multispectral satellite images, and possibly to perform the classification task directly on compressed data. We propose a multiresolution approach based on a special class of adaptive wavelets which allows the extraction of salient features without loosing accuracy.

关键词： Feature extraction Wavelet transforms Error analysis Image coding Satellites Remote sensing

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The toolkit and platform for biometric information processing

The toolkit and platform for biometric information processin...

引用

International conference on Computer Supported Cooperative Work in Design

作者： Xiaoguang He Jie Tian Yuliang He Jian Xue Xin Yang Center for Biometrics Research and Testing Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy and Sciences Beijing China

A biometric information processing toolkit (BITK) is designed and implemented to support the biometrics research, e.g. algorithm development and performance evaluation. BITK is an object-oriented C++ software development toolkit (SDK), and it provides a consistent, flexible and reusable framework to integrate algorithms, data structures, and visualization methods. In addition, an application platform based on BITK (BITKAPP) is developed. BITKAPP makes the best of BITK to serve the biometrics researchers with its friendly user interface and the plug-in architecture. The meaningful applications of the toolkit and platform confirm that they effectively support the biometrics research.

关键词： Biometrics Information processing Fingerprint recognition Filters Helium Algorithm design and analysis visualization Application software User interfaces Computer architecture

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Grid computing and beyond: The context of dynamic data driven applications systems

Grid computing and beyond: The context of dynamic data drive...

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作者： Darema, Frederica National Science Foundation Arlington VA 22230 United States

The advent of Grid computing has enhanced our capabilities to model and simulate complex systems arising in scientific, engineering, and commercial applications. The premise of Grid computing has been "on-demand" availability of computational resources to an application as needed, in the same manner as electricity is provided readily through electrical power grids. The computational grid (or simply the "Grid") entails ubiquitous access to resources (local or remote), such as computation and communication resources, as well as access to storage systems and visualization systems. As Grid computing technologies mature, it behooves to look beyond the current capabilities, into more advanced future environments. The environments of interest here are the enhanced capabilities that can be created by the paradigm of Dynamic data Driven Applications Systems (DDDAS). DDDAS entails the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. The DDDAS concept offers the promise of improving application models and methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. Enabling this synergistic feedback and control loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms stable under dynamic data injection and steering conditions, and new systems software and computational infrastructure capabilities. Recent advances in complex applications and the advent of Grid computing and sensor systems are some of the technologies that make it timely to embark in developing DDDAS capabilities. DDDAS environments extend the current notion of Grid infrastructure to also include the measurement systems in an integrated and synergistic way. DDDAS environments require support and services that go beyond the current Grid services in terms of t

关键词： Computational methods

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The Research of data Mining Based on Extension Sets

The Research of Data Mining Based on Extension Sets

引用

International conference on Information Technology and Applications, ICITA

作者： Qing Lu Yongquan Yu Institute of Intelligent Engineering Guangdong University of Technology Guangzhou China

ISBN: (纸本)0769523161

Extension data mining is a new method that is based on the extension analysis method of extenics. Extenics is a new disciplinary and a new branch of artificial intelligence. data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization. This paper presents how to deal with multiple data formats and unify data representation based on extenics

关键词： data mining Artificial intelligence Virtual colonoscopy Visual databases Association rules Mathematics data engineering Electronic mail Statistics Pattern recognition

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A Camera-Projector System for Real-Time 3D Video

A Camera-Projector System for Real-Time 3D Video

引用

IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： M.B. Vieira L. Velho Asla Sa P.C. Carvalho IMPA-Instituto de Matematica Pura e Aplicada Rio de Janeiro Brazil

In this paper, we describe a real-time 3D video system that is based on active stereo. Combining a NTSC standard camera/ projector equipment and a suitable color code, the geometric and photometric information of a scene is robustly obtained in 30fps. Our main motivation to develop this system is to create a platform for investigating the issues that will be posed by the next generation of digital video and how it can shape up new media.

关键词： Real time systems Cameras Stereo vision Layout Robustness Image analysis data mining data processing data visualization Hardware

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IT-Supported visualization of Knowledge Community Structures

IT-Supported Visualization of Knowledge Community Structures

引用

Annual Hawaii International conference on System Sciences (HICSS)

作者： M. Trier Faculty of Computing Sciences and Electrical Engineering Technical University Berlin Berlin Germany

In the first part of this article, Communities of Practice are conceptually positioned as a very important and successful element of corporate Knowledge Management. By utilizing IT platforms they enable a direct connection of knowledge workers and the transfer and reuse of tacit expertise to geographically remote business problems. Although current Community Software provides its members with many sophisticated features, facilitators or moderators still lack functionalities to monitor, evaluate and communicate the development of their expert networks. After discussing the requirements of this special target group, this contribution concentrates on electronic discussions and proposes a software system for automatically analyzing the structure and value of Knowledge Communities by extracting available electronic data about their communication network. This includes the entities employee, topic, and document and their many relationships. Insightful structural visualizations based on theories of Network analysis are introduced. They can be accessed and manipulated in a Management Cockpit to improve the transparency in communities.

关键词： Knowledge management Instruments Remote monitoring Software systems data mining Communication networks data visualization Information processing Procurement Information analysis

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A geometric framework to visualize fuzzy-clustered data

A geometric framework to visualize fuzzy-clustered data

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Computer Science Society (SCCC) International conference Chilean FLAGGED

作者： Yuanquan Zhang L. Rueda School of Computer Science University of Windsor Windsor ONT Canada

Fuzzy clustering methods have been widely used in many applications. These methods, including fuzzy k-means and expectation maximization, allow an object to be assigned to multi-clusters with different degrees of membership. However, the memberships that result from fuzzy clustering algorithms are difficult to analyze and visualize, and usually are converted to 0-1 memberships. In this paper, we propose a geometric framework to visualize fuzzy-clustered data. The scheme provides a geometric visualization by grouping the objects with similar cluster membership, and shows clear advantages over existing methods, demonstrating its capabilities for viewing and navigating inter-cluster relationships in a spatial manner.

关键词： data visualization Clustering algorithms Computer science Clustering methods Application software Algorithm design and analysis Navigation Speech Self organizing feature maps DNA

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Supervised LLE in ICA Space for Facial Expression Recognition

Supervised LLE in ICA Space for Facial Expression Recognitio...

引用

International conference on Neural Networks and Brain (ICNNB)

作者： Qijun Zhao D. Zhang Hongtao Lu Departinent of Computer Scicence and Engineering Shanghai Jiaotong University Shanghai China Department of Computing Hong Kong Polytechnic University Hong Kong China Department of Computer Scicence and Engineering Shanghai Jiaotong University Shanghai China

Locally linear embedding (LLE) is an unsupervised nonlinear manifold learning algorithm. It performs well in visualizing data yet has a very poor recognition rate in facial expression recognition. In this paper, to improve the performance of LLE in facial expression recognition, we first employ the independent component analysis (ICA) technique to preprocess the face images such that they are represented by some independent components and some noise is filtered from them. We then propose a supervised LLE (SLLE) algorithm to learn the hidden manifold. SLLE constructs the neighborhood graphs for the data according to the Euclidean distances between them and the cluster information of them. Its embedding step is the same as that of LLE. Finally, we use a generalized regression neural network (GRNN) to learn the implicit nonlinear mapping from the ICA space to the embedded manifold. Experiments on the JAFFE database show promising results

关键词： Independent component analysis Face recognition Embedded computing data visualization Image recognition Humans Principal component analysis Linear discriminant analysis Image sequences Manifolds

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Development of an artificial intelligence system for detection and visualization of auto theft recovery patterns

Development of an artificial intelligence system for detecti...

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IEEE International conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS)

作者： O. Kursun K. Reynolds R. Eaglin Bing Chen M. Georgiopoulos Department of Engineering Technology University of Central Florida Orlando FL USA Department of Criminal Justice and Legal Studies University of Central Florida Orlando FL USA Department of Electrical and Computer Engineering University of Central Florida Orlando FL USA

Auto theft is the most expensive property crime that is on the rise across the nation. The prediction of auto drop-off locations can increase the probability of offender apprehension. For successful prediction, first the patterns of thefts are identified. Then, a prototype expert system successfully identified embedded drop-off location clusters that were previously unknown to investigators. The system was developed using the expert knowledge of auto theft investigators along with spatial and temporal auto theft event data. Drop-off clusters were identified and validated. A map interface allows the user to visualize the feature clusters and produce detailed reports. Such GIS applications give us the ability to attain a geographical perspective of incidents within the community, thus help law enforcement officers discover the patterns of incidents and take necessary measures to prevent them

关键词： Artificial intelligence Vehicles Geographic Information Systems Law enforcement Pattern analysis Legal factors Prototypes Expert systems data visualization Statistics

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Feature Selection for Cluster analysis: an Approach Based on the Simplified Silhouette Criterion

Feature Selection for Cluster Analysis: an Approach Based on...

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International conference on Computational Intelligence for Modelling, Control and Automation, and International conference on Intelligent Agents, Web Technologies and Internet Commerce

作者： E.R. Hruschka T.F. Covoes Catholic University of Santos Unisantos Sao Paulo Brazil

This paper explores the problem of selecting relevant features for clustering, assuming that the number of clusters is not known a priori. The number of clusters and the subset of relevant features are usually inter-related. From this standpoint, we propose an exploratory data analysis method that considers the relationships between these two aspects. Empirical results in a number of synthetic and bioinformatics datasets show that the proposed approach can allow both reducing the number of features and providing good estimations of the number of clusters

关键词： Clustering algorithms data analysis Clustering methods Information filtering Information filters Bioinformatics Supervised learning Text mining Gene expression data visualization

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