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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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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

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

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Edge detection using clustering algorithms

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WSEAS Transactions on Computers 2005年第5期4卷 417-423页

作者： Sirin, Tuba Saglam, Mehmet Izzet Erer, Isin Gokmen, Muhittin Informatics Institute Advanced Technologies in Engineering Computer Science and Engineering Istanbul Technical University Istanbul 34469 Turkey Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Istanbul Technical University Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Electronics and Communication Department Istanbul Technical University Istanbul 34469 Turkey Faculty of Electrical and Electronic Engineering Computer Engineering Department Istanbul Technical University Istanbul 34469 Turkey

Edge detection is an important topic in image processing and a main tool in pattern recognition and image segmentation. Many edge detection techniques are available in the literature. 'A number of recent edge detectors are multiscale and include three main processing steps: smoothing, differentiation and labeling' (Ziau and Tabbone, 1997). This paper, presents a proposed method which is suitable for edge detection in images. This method is based on the use of the clustering algorithms (Self-Organizing Map (SOM), K-Means) and a gray scale edge detector (Canny, Generalized Edge Detector (GED)). It is shown that using the grayscale edge detectors may miss some parts of the edges which can be found using the proposed method.

关键词： Edge detection

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MASS INCARCERATION: WHO PAYS THE PRICE FOR CRIMINAL OFFENDING?

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Criminology & Public Policy 2006年第2期3卷

作者： TRACEY L. MEARES Tracey Meares received her B.S. in General Engineering from the University of Illinois and her J.D. from The Law School. She joined the University of Chicago faculty in 1994 after serving as an Honors Program Trial Attorney in the Antitrust Division of the United States Department of Justice. Prior to serving as a Department of Justice prosecutor Ms. Meares clerked for Judge Harlington Wood Jr. of the US. Court of Appeals for the Seventh Circuit. Ms Meares's teaching and research interests center on criminal procedure and criminal law policy with a particular emphasis on empirical investigation of these subjects. In addition to teaching at The Law School Ms Meares has an appointment as a Research Fellow at the American Bar Foundation. She is also a faculty member of the University of Chicago Center for the Study of Race Politics and Culture.

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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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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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Chopping of Oil Palm Empty Fruit Bunch Required to Produce Compost Material

Chopping of Oil Palm Empty Fruit Bunch Required to Produce C...

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国际农业工程大会

作者： Hadi Suryanto Lecturer and Head Study Program of Agricultural Engineering Faculty of Agriculture Andalas University PO Box 87 Padang Indonesia.

In Indonesia, there is about 2 million ha of oil palm plantation with 25-ton/ha/year fruit bunch. Besides oil, processing of fruit bunch also produces empty bunch of about 25%. This empty bunch is usually burned at th... 详细信息

关键词： Chopping of Oil Palm Empty Fruit Bunch Required to Produce Compost Material

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A Factorized High Dimensional Model Representation on the Partitioned Random Discrete Data

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Applied Numerical Analysis & Computational Mathematics 2004年第1期1卷

作者： M. Alper Tunga Metin Demiralp Department of Computer Science and Engineering Engineering Faculty Işįk University Maslak 34398 İstanbul Turkey Computational Science and Engineering Program Informatics Institute İstanbul Technical University Maslak 80626 İstanbul Turkey Phone: +90 212 285 70 82 Fax: +90 212 285 70 73

The main purpose of this work is to obtain the general structure of a product type of multivariate function when the values of the function are given randomly at the nodes of a hyperprism. When the dimensionality of multivariate interpolation and the number of the data sets increase unboundedly, many problems can be encountered in the standard numerical methods. Factorized High Dimensional Model Representation (FHDMR) is used to obtain the general structure of this given multivariate function. The components of FHDMR are determined by using the components of the Generalized High Dimensional Model Representation (GHDMR).The given random discrete data is partitioned by GHDMR method. This partitioned data produces a structure for the multivariate function by using single variable Lagrange interpolation formula including only the constant term and the univariate terms of the GHDMR components. Finally, a general structure is obtained via FHMDR by using these components. By this way, the multidimensionality of a multivariate interpolation is approximated by lower dimensional interpolations like univariate ones. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

关键词： Multivariate functions multivariate representations approximation factor analysis interpolation multidimensional problems

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DCT and wavelet based image compression in satellite images

DCT and wavelet based image compression in satellite images

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International Conference on Recent Advances in Space Technologies, RAST 2003

作者： Hacihaliloglul, Llker Karta, Mesut Satellite Communication and Remote Sensing Graduate Program Advanced Technologies in Engineering Informatics Institute Istanbul Technical University Maslak Istanbul34469 Turkey Electronic and Communication Department Faculty of Electrical and Electronic Engineering Istanbul Technical University Maslak Istanbul34469 Turkey

ISBN: (纸本)0780381424

Synthetic aperture radar (SAR) and SPOT images are becoming increasingly important and abundant in a variety of remote sensing and tactical applications. Thus, there is a strong interest in developing data encoding and decoding algorithms that can obtain higher compression ratios while keeping image quality to an acceptable level. This study aims to compare most of the well-known compression techniques namely discrete cosine transform and discrete wavelet transform. It investigates RADARSAT and SPOT images of different regions of different characteristics. RADARSAT-I fine and standard beam mode images of the different regions in Istanbul are used to test the performance of the compression algorithms. The regions, which have been investigated, were sea areas, forest areas, built environment - residential and industrial areas which define different patterns of urban land use. The studies showed that homogeneous areas like forest and sea gave better compression results compared to heterogeneous areas like industrial and environmental. The second purpose of this study is to compare the two compression algorithms. The discrete wavelet based algorithm gave much better results compared to the discrete cosine transform based algorithm. The results changed according to the quantization process and the transform coding algorithm. © 2003 IEEE.

关键词： Discrete wavelet transforms

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A spatial filtering approach to the classification of multispectral remote sensing data

A spatial filtering approach to the classification of multis...

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International Conference on Recent Advances in Space Technologies (RAST)

作者： H. Karakahya B. Yazgan Informatics Institute Advanced Technologies in Engineering Satellite Communication and Remote Sensing Program Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronic Engineering Electronics and Communication Department Istanbul Technical University Istanbul Turkey

Spatial information is an essential key to the classification of remote sensing images. In this paper, a filtering approach, which tries to exploit the spatial component of the remote sensing data, is described and it... 详细信息

关键词： Filtering Remote sensing Data mining Remote monitoring Spatial filters Testing Data engineering Pixel Image segmentation Classification algorithms

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