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13th European Signal Processing Conference, EUSIPCO 2005

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gokmen, Muhittin Ersoy, Okan Informatics Institute Advanced Technologies in Engineering Computer Science and Engineering Maslak Istanbul 34469 Turkey Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Maslak Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Electronics and Communication Department Maslak Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Computer Engineering Department Istanbul Technical University Maslak Istanbul 34469 Turkey Purdue University School of Electrical and Computer Engineering West Lafayette IN United States

ISBN: (纸本)1604238216

Most edge detection algorithms include three main stages: smoothing, differentiation, and labeling. In this paper, we evaluate the performance of algorithms in which competitive learning is applied first to enhance edges, followed by an edge detector to locate the edges. In this way, more detailed and relatively more unbroken edges can be found as compared to the results when an edge detector is applied alone. The algorithms compared are K-Means, SOM and SOGR for clustering, and Canny and GED for edge detection. Perceptionally, best results were obtained with the GED-SOGR algorithm. The SOGR is also considerably simpler and faster than the SOM algorithm.

关键词： Edge detection

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Edge detection in multispectral remote sensing images

Edge detection in multispectral remote sensing images

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RAST 2005 - 2nd International Conference on Recent Advances in Space Technologies

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gökmen, Muhittin Ersoy, Okan Istanbul Technical University Informatics Institute Advanced Technologies in Engineering Maslak/Istanbul Turkey Istanbul Technical University Informatics Institute Satellite Communication and Remote Sensing Program Maslak/Istanbul Turkey Istanbul Technical University Faculty of Electrical and Electronic Engineering Electronics and Communication Department Maslak/Istanbul Turkey Istanbul Technical University Faculty of Electrical and Electronic Engineering Computer Engineering Department Maslak/Istanbul Turkey Purdue University School of Electrical and Computer Engineering West Lafayette IN United States

ISBN: (纸本)0780389778

In this paper, we discuss edge detection by first using a clustering algorithm followed by a known edge detection filter such as Canny or Generalized Edge Detector (GED). We developed a new clustering method called Self-Organizing Global Ranking (SOGR). Comparative results with multispectral satellite images including SOGR, SOM and K-Means clustering methods are discussed. The results show that the two-stage algorithms are better than single stage edge detector algorithms. © 2005 IEEE.

关键词： Edge detection

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E-NOSE response classification of sewage odors by neural networks and fuzzy clustering

E-NOSE response classification of sewage odors by neural net...

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First International Conference on Natural Computation, ICNC 2005

作者： Önkal-Engin, Güleda Demir, Ibrahim Engin, Seref N. Gebze Institute of Technology Department of Environmental Engineering Gebze 41400 Kocaeli Turkey Environmental Informatics and Control Program Warnel School of Forest Resources University of Georgia Athens GA 30602 United States Department of Electrical Engineering Yildiz Technical University 34800 Besiktas Istanbul Turkey

Each stage of the sewage treatment process emits odor causing compounds and these compounds may vary from one location in a sewage treatment works to another. In order to determine the boundaries of legal standards, reliable and efficient odor measurement methods need to be defined. An electronic NOSE equipped with 12 different polypyrrole sensors is used for the purpose of characterizing sewage odors. Samples collected at different locations of a WWTP were classified using a fuzzy clustering technique and a neural network trained with a back-propagation algorithm. © Sringer-Verlag Berlin Heidelberg 2005.

关键词： Sewage treatment

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A comparative evaluation of competitive learning algorithms for edge detection enhancement

A comparative evaluation of competitive learning algorithms ...

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European Signal Processing Conference (EUSIPCO)

作者： Tuba Sirin Mehmet Izzet Saglam Isin Erer Muhittin Gokmen Okan Ersoy Informatics Institute Computer Science and Engineering Informatics Institute Satellite Communication and Remote Sensing Program Faculty of Electrical and Electronic Engineering Electronics and Communication Department Faculty of Electrical and Electronic Engineering Istanbul Technical University Maslak Istanbul TURKEY Purdue University School of Electrical and Computer Engineering West Lafayette Indiana USA

关键词： Image edge detection Image segmentation Vectors Abstracts Filtering algorithms Erbium Training

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Handling missing attribute values in preterm birth data sets

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10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2005

作者： Grzymala-Busse, Jerzy W. Goodwin, Linda K. Grzymala-Busse, Witold J. Zheng, Xinqun Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS 66045 United States Institute of Computer Science Polish Academy of Sciences 01-237 Warsaw Poland Nursing Informatics Program Duke University Durham NC 27710 United States Filterlogix Lawrence KS 66049 United States

ISBN: (纸本)3540286608

The objective of our research was to find the best approach to handle missing attribute values in data sets describing preterm birth provided by the Duke University. Five strategies were used for filling in missing attribute values, based on most common values and closest fit for symbolic attributes, averages for numerical attributes, and a special approach to induce only certain rules from specified information using the MLEM2 approach. The final conclusion is that the best strategy was to use the global most common method for symbolic attributes and the global average method for numerical attributes. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Data handling

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Handling Data Sparseness in Gene Network Reconstruction

Handling Data Sparseness in Gene Network Reconstruction

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： G.B. Bezerra T.V. Barra F.J. von Zuben L.N. de Castro Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program on Informatics Catholic University of Santos Sao Paulo Brazil

One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.

关键词： Intelligent networks Bayesian methods Genetics Bioinformatics Computer networks Reverse engineering Cellular networks Noise robustness Laboratories Data engineering

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Appointments for 2005-2006 term

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2005年第7期13卷 773-782页

作者： Ranganathan, Nagarajan Agrawal, Vishwani D. Chakradhar, Srimat T. Chakrabarty, Krishnendu Courtois, Bernard DeMara, Ronald Hu, Xiaobo Sharon Ismail, Yehea I. Jha, Niraj K. John, Lizy Kurian Ker, Ming-Dou Koren, Israel Liu, Bin-Da Marculescu, Diana Marculescu, Radu Narayanan, Vijaykrishnan Nassif, Sani R. Nowick, Steven M. Sapatnekar, Sachin S. Sherlekar, Sunil Sylvester, Dennis Vemuri, Ranga Pham, Michael Department of Computer Science and Engineering University of South Florida Tampa FL 33620 United States Department of Computer Science and Engineering Nanomaterials and Nanomanufacturing Research Center University of South Florida Tampa United States IEEE United States Systems United States Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ United States IETE India ACM NEC Laboratories America Inc. Princeton NJ United States Duke University Durham NC United States ACM SIGDA Sigma Xi United States Laboratory of Techniques of Informatics and Microelectronics for Computer Architectecture ASME IMAPS IEEE Computer Society University of Central Florida Orlando United States Computer Engineering Program SEECS Department of Computer Science and Engineering University of Notre Dame Notre Dame IN United States Northwestern University Evanston IL United States Department of Electrical Engineering Princeton University Princeton NJ United States Association for Computing Machinery Austin United States UT Austin Engineering Foundation United States Department of Electronics Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering University of Massachusetts Amherst United States Department of Electrical Engineering Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States Electrical and Computer Engineering Faculty Carnegie Mellon University Pittsburgh PA United States Computer Science and Engineering Department Pennsylvania State University University Park United States IBM Austin Research Laboratory Austin TX United States Columbia University New York NY United States Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States Tata Consultancy Services Centre of Excellence for Embedded Systems India Department of Electrical Engineering IIT Bombay India Department of Electrical Engineering Uni

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Universal designs versus assistive technologies: Research agendas and practical applications

Universal designs versus assistive technologies: Research ag...

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ASSETS 2005 - The Seventh International ACM SIGACCESS Conference on Computers and Accessibility

作者： Law, Chris Jacko, Julie Peterson, Bill Tobias, Jim Information Systems Department UMBC 1000 Hilltop Circle Baltimore MD 21227 United States Department of Biomedical Engineering Georgia Institute of Technology 313 Ferst Drive NW Atlanta GA 30332 United States Section 508 Program Management. Office Department of Homeland Security 301 7th Street SW Washington DC 20407 United States Inclusive Technologies 37 Miriam Drive Matawan NJ 07747 United States Georgia Tech. United States Laboratory for HCI and Health Care Informatics United States Section 508 Program Management Office United States Interagency Committee on Disability Research Subcommittee on Technology Inclusive Technologies United States United States

No abstract available

ISBN: (纸本)1595931597

No abstract available

关键词： Assistive Technologies Research and Practice Universal Design

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An Exponential Matrix Product Based Representation for Generalized Hypergeometric Functions of TypepFp

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Applied Numerical Analysis & Computational Mathematics 2005年第1期2卷

作者： N. A. Baykara İrem Yaman Metin Demiralp Mathematics Department Faculty of Sciences and Letters Marmara University Göztepe 34722 İstanbul Turkey Group for Science and Methods of Computing Computational Science and Engineering Program Informatics Institute İstanbul Technical University Maslak 34469 İstanbul Turkey Phone: +90 212 285 70 77 Fax: +90 212 285 70 73 Phone: +90 212 285 70 82 Fax: +90 212 285 70 73

In this work, a new representation is developed for generalized hypergeometric functions of type p F p . To this end a first order vector differential equation is constructed in a way such that the derivative of the unknown vector has unit matrix coefficient. The solution vector differential equation is rewritten in the form of a series expansion. An infinite process of factor extractions and annihilations is employed to construct finally a vector differential equation that can be easily solved. Truncation of this scheme can be used to get approximations to hypergeometric functions of type p F p . A simple, yet meaningful, implementation seems to give quite promising results. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

关键词： Exponential matrix factorization generalized hypergemetric function

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An edge detection method using 2-D autoregressive lattice prediction filters for remotely sensed images

An edge detection method using 2-D autoregressive lattice pr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： R. Gurcan I. Erer S. Kent Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Istanbul Technical University Maslak Istanbul Turkey Istanbul Technical University Maslak Istanbul Turkey

Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Edge detecting an image significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Edge detection is useful for segmentation, registration, and identification of objects in remote sensing images. Two dimensional lattice filters have been shown to be useful in many applications such as multidimensional spectral estimation, image data compression, high-resolution radar imaging, and removal of correlated clutter to enhance the detection ability of small objects in images. In this work, lattice filters are used for detecting the edges in remote sensing images. Lattice filter can be used to predict the correlated parts in an image and the resulting error (the output of the filter) will be edges. Edge detection results have been compared with other conventional edge detection methods as well as wavelet based methods. Results show that the proposed method is a good candidate for edge detection problem in remotely sensed images

关键词： Image edge detection Lattices Remote sensing Radar detection Image processing Information filtering Information filters Image segmentation Multidimensional systems Data compression

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