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International Conference on the Digital Society

作者： M. Mitra S. Mitra J.N. Bera R. Gupta B.B. Chaudhuri Department of Applied Physics University of Calcutta India Computer Vision and Pattern Recognition Unit Kolkata India

ISBN: (纸本)9781424431342;0769527604

This paper describes a portable diagnostic telecardiology system, aimed to benefit the rural people of a third world country like India. The designed system consists of two major blocks; the first one is required to be carried to the patient home, named 'Portable Telecardiology Kit'. The second one, named 'Automated Cardiac Signal Processor', a PC based system, to be permanently placed at the nearest rural health care center. The 'Portable Telecardiology Kit' contains a portable ECG machine, a dedicated microcontroller-based full duplex communication system, both interfaced, in a single enclosure. This kit will convert the ECG signal along with a tag page (containing different information regarding patient's health and history of illness) to a digitized serial electromagnetic wave. The signal may be transmitted over a distance of about 7-10 km through a cordless phone under full duplex mode. The PC-based system, after receiving the transmitted signal from patient site, extract the ECG signal after proper filtering, and then pass the signal through an in-built knowledge-base. Finally, a rule-based rough set decision system is generated for the development of an inference engine for disease identification from these time-plane features. The system will generate a report indicating the preliminary level abnormalities, and the precautions that can be adopted at an early stage. The entire report is then sent to the Portable kit at the patient bed-site.

关键词： Electrocardiography Telemedicine Medical services Artificial neural networks Set theory pattern recognition Fuzzy neural networks Diseases Discrete wavelet transforms Radiofrequency amplifiers

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Gene Expression Clustering: a Novel Graph Partitioning Approach

Gene Expression Clustering: a Novel Graph Partitioning Appro...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yanhua Chen Ming Dong Manjeet Rege Machine Vision and Pattern Recognition Laboratory Department of Computer Science Wayne State University Detroit MI USA

In order to help understand how the genes are affected by different disease conditions in a biological system, clustering is typically performed to analyze gene expression data. In this paper, we propose to solve the clustering problem using a graph theoretical approach, and apply a novel graph partitioning model -isoperimetric graph partitioning (IGP), to group biological samples from gene expression data. The IGP algorithm has several advantages compared to the well-established spectral graph partitioning (SGP) model. First, IGP requires a simple solution to a sparse system of linear equations instead of the eigen-problem in the SGP model. Second, IGP avoids degenerate cases produced by spectral approach to achieve a partition with higher accuracy. Moreover, we integrate unsupervised gene selection into the proposed approach through two-way ordering of gene expression data, such that we can eliminate irrelevant or redundant genes in the data and obtain an improved clustering result. We evaluate our approach on several well-known problems involving gene expression profiles of colon cancer and leukemia subtypes. Our experiment results demonstrate that IGP constantly outperforms SGP and produces a better result that is closer to the original labeling of sample sets provided by domain experts. Furthermore, the clustering accuracy is improved significantly when IGP is integrated with the unsupervised gene (feature) selection.

关键词： Gene expression Biological system modeling Diseases Biological systems Data analysis Performance analysis Clustering algorithms Partitioning algorithms Equations Colon

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Incorporating User Provided Constraints into Document Clustering

Incorporating User Provided Constraints into Document Cluste...

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IEEE International Conference on Data Mining (ICDM)

作者： Yanhua Chen Manjeet Rege Ming Dong Jing Hua Department of Computer Science Machine Vision and Pattern Recognition Laboratory Detroit MI USA Department of Computer Science Graphics and Imaging Laboratory Detroit MI USA

Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.

关键词： Iterative algorithms Clustering algorithms Symmetric matrices Data mining Databases Clustering methods Couplings Machine vision pattern recognition computer graphics

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Benchmarking 3D reconstructions from Next Best View planning

Benchmarking 3D reconstructions from Next Best View planning

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10th IAPR Conference on Machine vision Applications, MVA 2007

作者： Munkelt, Christoph Trummer, Michael Denzler, Joachim Wenhardt, Stefan Fraunhofer IOF Albert-Einstein-Straße 7 07745 Jena Germany Department of Computer Vision Friedrich Schiller University of Jena Ernst-Abbe-Platz 2 07743 Jena Germany Department of Pattern Recognition Friedrich Alexander University of Erlangen Martensstr. 3 91058 Erlangen Germany

ISBN: (纸本)9784901122078

The problem of planning the Next Best View (NBV) still poses many questions. However, the achieved methods and algorithms are hard to compare, since researchers use their own test objects for planning and reconstruction and compute specific quality measures. Consequently, these numbers make different statements about different objects. Thus, the quality of the results and the performance of the methods are not easily comparable. In order to mend this lack of measure and comparability, this paper suggests a test object together with a reference benchmark. These allow comparison of reconstruction results from different NBV algorithms achieved with different techniques and various kinds of sensors.

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Text/Graphics Separation in Color Maps

Text/Graphics Separation in Color Maps

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International Conference on Computing: Theory and Applications, ICCTA

作者： Partha Pratim Roy Josep Llados Umapada Pal Computer Science Department Universitat Autònoma de Barcelona Barcelona Bellaterra Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India

Text/graphics separation in document image analysis is one of the main concerns in present research work. The complexity enhances when both text and graphics overlap in the context of maps in color images. This paper discusses a number of improvements to text/graphics separation methods to make it suitable for maps. Emphasize is given to the overlapping regions of text and graphics. It also discusses a method of color separation using clustering method for the purpose of text/graphics separation

关键词： computer graphics Image color analysis Image segmentation Histograms Bit error rate computer vision Text analysis Clustering methods Engineering drawings Testing

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Robust real-time tracking for visual surveillance

Eurasip Journal on Advances in Signal Processing

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Eurasip Journal on Advances in Signal Processing 2007年 2007卷

作者： Thirde, David Borg, Mark Aguilera, Josep Wildenauer, Horst Ferryman, James Kampel, Martin School of Systems Engineering Computational Vision Group University of Reading Reading RG6 6AY United Kingdom Computer Science Department Pattern Recognition and Image Processing Group Vienna University of Technology Vienna 1040 Austria

This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve tracking of objects that are in close proximity. The four component modules described in this paper are (i) motion detection using a layered background model, (ii)object tracking based on local appearance, (iii) hierarchical object recognition, and (iv) fused multisensor object tracking using multiple features and geometric constraints. This integrated approach to complex scene tracking is validated against a number of representative real-world scenarios to show that robust, real-time analysis can be performed. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.

关键词： Tracking (position)

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Kernel-based recognition of human actions using spatiotemporal salient points

Kernel-based recognition of human actions using spatiotempor...

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2006 Conference on computer vision and pattern recognition Workshops

作者： Oikonomopoulos, A. Patras, I. Pantic, M. Computing Department Imperial College London Computer Vision and Pattern Recognition Group University of York

ISBN: (纸本)0769526462

This paper addresses the problem of human action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. We detect the spatiotemporal salient points by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. We derive a suitable distance measure between the representations, which is based on the Chamfer distance, and we optimize this measure with respect to a number of temporal and scaling parameters. In this way we achieve invariance against scaling, while at the same time, we eliminate the temporal differences between the representations. We use Relevance Vector Machines (RVM) in order to address the classification problem. We propose new kernels for use by the RVM, which are specifically tailored to the proposed spatiotemporal salient point representation. The basis of these kernels is the optimized Chamfer distance of the previous step. We present results on real image sequences from a small database depicting people performing 19 aerobic exercises. © 2006 IEEE.

关键词： pattern recognition

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On-ine variational estimation of dynamical fluid flows with physics-based spatio-temporal regularization

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28th Symposium of the German Association for pattern recognition, DAGM 2006

作者： Ruhnau, Paul Stahl, Annette Schnörr, Christoph Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim D-68131 Mannheim Germany

ISBN: (纸本)3540444122

We present a variational approach to motion estimation of instationary fluid flows. Our approach extends prior work along two directions;(i) The full incompressible Navier-Stokes equation is employed in order to obtain a physically consistent regularization which does not suppress turbulent flow variations, (ii) Regularization along the time-axis is employed as well, but formulated in a receding horizon manner contrary to previous approaches to spatio-temporal regularization. This allows for a recursive on-line (non-batch) implementation of our estimation framework. Ground-truth evaluations for simulated turbulent flows demonstrate that due to imposing both physical consistency and temporal coherency, the accuracy of flow estimation compares favourably even with optical flow approaches based on higher-order div-curl regularization. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Flow of fluids

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Human-focused computer vision applications

Human-focused computer vision applications

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International Conference on computer Graphics, Imaging and Visualisation, CGIV'06

作者： Piccardi, Massimo Department of Computer Systems Faculty of IT University of Technology Sydney Australia UTS Computer Vision Research Group IEEE IEEE Computer Society International Association for Pattern Recognition

ISBN: (纸本)0769526063

Recent years have seen an increasing number of computer vision applications focusing on humans as their objects of interest. Such applications include video surveillance, domotics, multimedia semantic annotation and indexing, human-computer interfaces, affective computing, just to cite a few. What exactly are their "objects of interest"? A broad range of human-related features: motion, gestures, actions, interactions, activities, attitudes, behaviours, identity. This keynote will offer a survey of this field and present some current work from the speaker and his collaborators in the areas of emotion recognition and people tracking within camera networks. © 2006 IEEE.

关键词： computer vision

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Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning

Region-based image annotation using asymmetrical support vec...

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2006 IEEE computer Society Conference on computer vision and pattern recognition, CVPR 2006

作者： Yang, Changbo Dong, Ming Hua, Jing Machine Vision and Pattern Recognition Lab. Department of Computer Science Wayne State University Detroit MI 48202 Department of Computer Science Wayne State University Detroit MI 48202

ISBN: (纸本)0769525970

In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets. © 2006 IEEE.

关键词： Image processing

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