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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是4021-4030 订阅

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The spectrum enhancement algorithm for feature extraction and pattern recognition

The spectrum enhancement algorithm for feature extraction an...

引用

Intelligent Robots and computer vision XXIII: Algorithms, Techniques, and Active vision

作者： Crosta, Giovanni Franco Inverse Problems and Mathematical Morphology Unit Department of Environmental Sciences Università Degli Studi Milano - Bicocca 1 Piazza delia Scienza I-20126 Milan Italy

The "enhanced spectrum" of an image g[&middot] is a fonction h[&middot] of wavenumber u obtained as follows. A reflection operation Q[&middot] is applied to g[&middot];the power spectral dens... 详细信息

ISBN: (纸本)0819460303

The "enhanced spectrum" of an image g[·] is a fonction h[·] of wavenumber u obtained as follows. A reflection operation Q[·] is applied to g[·];the power spectral density | G[u]| 2 of Q[g[·]] is converted to the Log scale and averaged over a suitable arc;the function s[·] of u alone is thus obtained, from which a known function, the "model" m[u], is subtracted: this yields h[u]· Models m(p)[·] used herewith have a roll-off like -10Log10[up]. As a consequence spectrum enhancement is a non-linear image filter which is shown to include partial spatial differentiation of Q[g[·]] of suitable order. The function h[·] emphasizes deviations of s[·] from the prescribed behaviour m (P)W[·]. The enhanced spectrum is used herewith as the morphological descriptor of the image after polynomial interpolation. Multivariate statistical analysis of enhanced spectra by means of principal components analysis is applied with the objective, of maximizing discrimination between classes of images. Recent applications to materials science, cell biology and environmental monitoring are reviewed.

关键词： Image enhancement

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Mean Shift-Based Edge Detection for Color Image

Mean Shift-Based Edge Detection for Color Image

引用

2005 International conference on Neural Networks and Brain(ICNN&B’05)

作者： Huimin Guo Ping Guo Qingshan Liu Department of Computer Science Beijing Normal University National Laboratory of Pattern Recognition Institute of Automation

ISBN: (纸本)0780394224

Edge detection is an important process in low level image *** the advent of powerful computers,it is now possible to move to the more computationally intensive realm of color image *** are many benefits in doing so including the increased amount of information for object location and ***,many proposed methods for color edge detection are computational expensive and are not very robust to the image *** this paper,a new method based on Mean Shift algorithm to detect edge in color images is *** gradient-ascent mean shift localizes edges accurately in the presence of noise and provides a good computational performance,being based on local operators. Experimental results show the effectiveness and robustness of proposed method.

关键词： Mean Shift-Based Edge Detection for Color Image image

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Road markings detection and tracking using Hough Transform and Kalman filter

Road markings detection and tracking using Hough Transform a...

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7th International conference on Advanced Concepts for Intelligent vision Systems, ACIVS 2005

作者： Voisin, Vincent Avila, Manuel Emile, Bruno Begot, Stephane Bardet, Jean-Christophe Laboratoire Vision et Robotique IUT de l'Indre 36000 Châteauroux France Vectra Road Engineering 78320 La Verrière France

ISBN: (纸本)354029032X

A lane marking tracking method using Hough Transform and Kalman Filtering is presented. Since the HT is a global feature extraction algorithm, it leads to a robust detection relative to noise or partial occlusion. The Kalman filter is used to track the roadsides which are detected in the image by this HT. The Kalman prediction step leads to predict the road marking parameters in the next frame, so we can apply the detection algorithm in smaller regions of interest, the computional cost is being consequently reduced. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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Support vector machines based data detection for holographic data storage systems

Support vector machines based data detection for holographic...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Ramamoorthy, Lakshmi Keskinoz, Mehmet Kumar, B. V. K. Vijaya Carnegie Mellon University Dept. of Electrical and Computer Engineering Pittsburgh PA 15213 United States Sabanci University Istanbul Turkey

ISBN: (纸本)0780388747

Nonlinear nature of Holographic Data Storage Systems (HDSS) suggests that nonlinear equalization and detection techniques may be beneficial. Complexity involved in nonlinear methods does not often make them practical solutions. Support Vector Machines (SVMs) are recently being studied for pattern recognition applications. We investigated linear SVM detection and observed that the Bit Error Rate (BER) using SVM for data detection on Linear Minimum Mean Squared Error (LMMSE) equalized holographically recorded and retrieved 2-D data pages is about 17% better than the simple threshold detection on unequalized pages. © 2005 ieee.

关键词： Vector quantization

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Joint multiuser detection and optimal spectrum balancing for digital subscriber lines

Joint multiuser detection and optimal spectrum balancing for...

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2005 ieee International conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Chan, Vincent M. K. Yu, Wei Electrical and Computer Engineering Department University of Toronto Toronto Canada

ISBN: (纸本)0780388747

This paper presents a joint multiuser detection and optimal spectrum balancing algorithm for heavily unbalanced crosstalk channels in digital subscriber line systems. To ensure detection of strong crosstalk only, the set of tones subject to multiuser detection must be chosen carefully. The problem of tone selection is highly coupled with the transmit power spectra and thus the optimal solution requires the two problems to be solved jointly. This paper makes use of the idea of dual decomposition to solve the above problem. By minimizing the Lagrangian dual of the primal problem, the joint tone selection and optimal spectral balancing problem can be solved globally and efficiently. Simulations show considerable bit rate increase when multiuser detection is performed with optimal spectrum balancing. ©2005 ieee.

关键词： pattern recognition

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Kernel based intrusion detection system 05

Kernel based intrusion detection system

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4th Annual ACIS International conference on computer and Information Science, ICIS 2005

作者： Kim, Byung-Joo Kim, Il-Kon Dept. of Info. and Comm. Youngsan University Yangsan Korea Republic of Dept. of Computer Science Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)0769522963

Recently applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. Selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not proper method for realtime intrusion detection system. In this paper, we develop the realtime intrusion detection system which combining on-line feature extraction method with Least Squares Support Vector Machine classifier. Applying proposed system to KDD CUP 99 data, experimental results show that it have remarkable performance compared to off-line intrusion detection system. © 2005 ieee.

关键词： pattern recognition systems

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Recovering the shape from texture using lognormal filters

Recovering the shape from texture using lognormal filters

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7th International conference on Advanced Concepts for Intelligent vision Systems

作者： Massot, C Hérault, J Lab Images & Signals Grenoble France

ISBN: (纸本)354029032X

How does the visual cortex extract perspective information from textured surfaces? To answer this question, we propose a biologically plausible algorithm based on a simplified model of the visual processing. First, new log-normal filters are presented in replacement of the classical Gabor filters. Particularly, these filters are separable in frequency and orientation and this characteristic is used to derive a robust method to estimate the local mean frequency in the image. Based on this new approach, a local decomposition of the image into patches, after a retinal pre-treatment, leads to the estimation of the local frequency variation all over the surface. The analytical relation between the local frequency and the geometrical parameters of the surface, under perspective projection, is derived and finally allows to solve the so-called problem of recovering the shape from the texture. The accuracy of the method is evaluated and discussed on different kind of textures, both regular and irregular, and also on natural scenes.

关键词： pattern recognition

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Jaw motion from EMG signals - A new bruxism monitor

Jaw motion from EMG signals - A new bruxism monitor

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3rd IASTED International conference on Medical Engineering 2005

作者： Fu, B. Beddoes, M.P. Peck, C.C. Ward, R.K. Hannam, A.G. Dept. of Electrical and Computer Engineering University of British Columbia Vancouver BC Canada Centre for Oral Health Westmead Hospital University of Sydney NSW 2006 Australia Department of Oral Physiology University of British Columbia Vancouver BC Canada

ISBN: (纸本)0889864780

Sleep bruxism is the involuntary and excessive clenching and grinding of teeth at night. It is one of the reasons that cause serious teeth damage and jaw muscle disorder and currently there is no definitive cure. Knowing actual jaw actions during bruxism will help in designing a more targeted treatment. This paper presents an EMG pattern recognition method to identify jaw movements. pattern recognition is carried out using backpropagation artificial neural networks (BPN) trained by supervised learning. Different feature extraction methods have been implemented. Results are presented to support the feasibility of the suggested approach.

关键词： Signal processing

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On the relation between dependence and diversity in multiple classifier systems

On the relation between dependence and diversity in multiple...

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ITCC 2005 - International conference on Information Technology: Coding and Computing

作者： Chen, Dechang Hua, Dong Sirlantzis, Konstantinos Ma, Xiaobin Preventive Medicine and Biometrics Uniformed Services University of the Health Sciences Bethesda MD 20814 United States Department of Computer Science George Washington University 801 22nd St. NW Washington DC 20052 United States Department of Electronics University of Kent Canterbury Kent CT2 7NT United Kingdom Computer Science and Engineering University of Minnesota 200 Union Street SE Minneapolis MN 55455 United States

ISBN: (纸本)0769523153

In this paper we investigate the issues of independence and diversity among individual classifiers participating in a multiple classifier fusion scheme. First we present a formal definition of statistically independent classifiers. Then we focus on testing the independence between two classifiers. Dependence of two classifiers leads to the conclusion that every ensemble of classifiers in which they participate is not an independent scheme. Previous studies have argued that independence of the classifiers infuses diversity in the multi-classifier system, which is directly related to improved performance. Consequently, we introduce a measure for the degree of diversity as expressed by the agreement among the classifiers' outputs in such an ensemble. A number of examples drawn from diverse domains in pattern recognition are also given to illustrate the relation between classifier dependence and diversity estimation. Our results suggest the measurement of the classifiers ' decisions agreement as an informative measure of the strength of association among dependent classifiers. © 2005 ieee.

关键词： Classification (of information)

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Extraction of a compact analytical data file from a complex, binary image for used in real time learning

Extraction of a compact analytical data file from a complex,...

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conference on vision Geometry XIII

作者： Hu, CLJ So Illinois Univ Carbondale IL 62901 USA

ISBN: (纸本)0819456489

For a binary image containing only curves (and lines) in a background infested by binary noises, (e.g., salt-and-pepper noise,) a very efficient way to extract the image data, and to save them in a very compact file for an accurate and complete image recall later, is very attractive to many image processing and pattern recognition systems. This paper reports the data-extraction method we developed recently for inputting a binary image to a special neural-network pattern recognition system, the noniterative, real-time learning system. We use an adaptive/tracking window to track the direction of a continuous curve in the binary image, and record the xycoordinates of all points on this curve until the window hits an end point, or a branch point, or the original starting point. By scanning this tracking window across the whole image frame, we can then segment the original binary image into many single curves. The xy's of points on each curve can then be analyzed by a curve fitting process, and the analytic data can be stored very compactly in an analog data file. This data file can be recalled very efficiently to reconstruct the original binary image, or can be used directly for inputting to a special neural network and for carrying out an extremely fast pattern learning process. This paper reports the image-processing steps, the programming algorithm, and the experimental results on this novel image extraction technique. It will be verified in each experiment by reconstructing the original image from the compactly extracted analog data file.

关键词： Binary data Analog electronics Image segmentation Neural networks Image processing pattern recognition Image analysis Detection and tracking algorithms computer programming Feature extraction

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