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检索条件"机构=Science and Technology on Blind Signal Processing Laboratory"

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GNSS illuminator based high range resolution algorithm in space-surface bistatic SAR

GNSS illuminator based high range resolution algorithm in sp...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jie Zhen Zhenhua Zhang Shunjun Wu Guoman Huang Chinese Academy of Surveying and Mapping Beijing China National Laboratory of Radar Signal Processing Xidian University Xi'an China No. 704 Institute Chinese Aerospace Science and Technology Corporation Beijing China

Space-surface bistatic synthetic aperture radar (SS-BSAR) has gained more and more researcher's interests. In this paper, we focus on improving range resolutions of GNSS illuminator based SS-BSAR with the method of spectrum synthesis of ultra-wide-band (UWB). Narrow bandwidth signals, such as GPS P code, can be spliced into a wider signal. Simulation results show that it can effectively increase signal processing bandwidth, improve range resolution and target recognition capabilities.

关键词： signal resolution Bandwidth Spaceborne radar Image resolution Global Navigation Satellite Systems Synthetic aperture radar

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Performance evaluation of Independent Component Analysis in an iris recognition system

Performance evaluation of Independent Component Analysis in ...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Imen Bouraoui Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Algiers Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

关键词： Iris recognition Feature extraction Entropy Iris Transforms Vectors Random variables

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Gaussian modeling and Discrete Cosine Transform for efficient and automatic palmprint identification

Gaussian modeling and Discrete Cosine Transform for efficien...

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International Conference on Machine and Web Intelligence (ICMWI)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Signal and Image Processing Laboratory Electronics and Computer Science Faculty University of Sciences and Technology Houari Boumediene Bab-Ezzouar Algeria School of Computing Engineering and Information Sciences Northumbria University Newcastle-upon-Tyne UK

Automatic personal identification using biometric information is playing a more and more important role in applications such as public security, access control, banking, etc. Palmprint identification is a subcategory of biometrics identification, which can efficiently used to identify the people. It is for this reason that palmprint-based identification is becoming increasingly popularity in recent years. In this paper, we present a novel scheme for palmprint identification using the multi-variate Gaussian Probability Density Function (GPDF) and two-dimensional Block based Discrete Cosine Transform (2D-BDCT). In this method, a palmprint is firstly divided into overlapping and equal-sized blocks, and then, applies the discrete cosine transform over each block. By using zigzag scan order (starting at the top-left) each transform block is reordered to produce the observation vector. Subsequently, we use the Gaussian probability density function for modeling the feature vector of each palmprint. Finally, Log-likelihood scores are used for palmprint matching. The proposed scheme is validated for their efficacy on PolyU palmprint database of 100 users. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification accuracy rate.

关键词： Feature extraction Discrete cosine transforms Databases Accuracy Hidden Markov models Probability density function

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Volume-based magnetic resonance brain image classification

Volume-based magnetic resonance brain image classification

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International Conference on Genetic and Evolutionary Computing

作者： Chiou, Yaw-Jiunn Chai, Jyh-Wen Chen, Clayton Chi-Chang Chen, Shih-Yu Chen, Hsian-Min Ouyang, Yen-Chieh Su, Wu-Chung Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

This paper develops a volume-based technique, called Volume Sphering Analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices-formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices. © 2010 IEEE.

关键词： Support vector machines

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Rhythmic component extraction considering phase alignment and the application to motor imagery-based brain computer interfacing

Rhythmic component extraction considering phase alignment an...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hiroshi Higashi Toshihisa Tanaka Yasue Mitsukura Department of Electrical and Electronic Engineering Tokyo University of Agriculture슠and슠Technology Japan Laboratory for Advanced Brain Signal Processing RIKEN Brain Science Institute Japan Graduate School of Bio-Applications and System Engineering Tokyo University of Agriculture슠and슠Technology Japan

We propose a novel method for extracting a rhythmically oscillating signal from EEG recordings including multiple source signals which have similar frequencies. The main application of this method is brain computer interfaces (BCI), which use rhythmically oscillating signals such as alpha, mu, and beta waves, as feature signals. It is difficult to separate those components and/or extract an command-related component when these feature signals span the same frequency band. The main idea is to assume that signals generated in different brain parts have different phases, even though they have the same frequency. This hypothesis is effectively incorporated with the previously proposed rhythmic component extraction (RCE) method, which successfully extracts a signal oscillating at a certain frequency from multi-channel sensor signals in the BCI application. The signal model is firstly given and then this novel extraction method is formulated as an optimization problem. We apply the proposed method for the classification of multi-channel EEG signals between imaginary left/right hand movement. Our experiment suggests that the proposed method is effective in feature extraction for motor-imagery based BCI.

关键词： Feature extraction Electroencephalography Electrodes Brain modeling Cost function Eigenvalues and eigenfunctions Rhythm

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Techniques for automatic magnetic resonance image classification

Techniques for automatic magnetic resonance image classifica...

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International Conference on Genetic and Evolutionary Computing

作者： Chen, Hsian-Min Chen, Shih-Yu Chai, Jyh Wen Chen, Clayton Chi-Chang Wu, Chao-Cheng Ouyang, Yen-Chieh Tsai, Ching Tsorng Yang, Ching-Wen Lee, San-Kan Chang, Chein-I Department of Biomedical Engineering Hungkuang University Taichung Taiwan Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 United States

ISBN: (纸本)9780769542812

Designing and developing automatic techniques for magnetic resonance images (MR) for data analysis is very challenging. One popular and public available method, FAST (FMRIB Automatic Segmentation Tool) has been widely used for automatic brain tissue segmentation for this purpose. This paper investigates limitations of this software algorithm on implementation and further develops a new approach to automatic MR brain tissue classification. The proposed new technique first implements an unsupervised training sample generation process (UTSGP) which includes a Pixel Purity Index (PPI) to generate an initial set of training samples that are further refined by a Support Vector Machine. The resulting training samples are then as a set of training samples for an Iterative Fisher's Linear Discriminant Analysis (IFLDA) which implements FLDA iteratively to improve classification. In order to conduct a fair comparison synthetic images are used for performance evaluation. Experimental results show that our proposed technique is superior in practical implementation to this software algorithm in several aspects of generalization ability, flexibility of choosing number of classes to be classified, avoidance of inconsistent results caused by different initial conditions. © 2010 IEEE.

关键词： Support vector machines

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NMF features for speech emotion recognition 09

NMF features for speech emotion recognition

引用

International Conference on Convergence and Hybrid Information technology 2009, ICHIT 2009

作者： Jeong, Kyungjoong Song, Jaiyoun Jeong, Hong Pohang University of Science and Technology Advanced Signal Processing Laboratory Pohang 790-784 Korea Republic of

ISBN: (纸本)9781605586625

There are numerous algorithms to detect emotion from speech signals. Among the algorithms, we selected spectral analysis and fused Non-negative Matrix Factorization (NMF) for obvios emotion classification. The algorithm has been tested in several different ways by varying NMF and the speech database. The experimental results show performance of 90% and classification success of 77% for speaker dependent and independent cases, respectively. © 2009 ACM.

关键词： Non-negative matrix factorization

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An image co-registration method for wide-swath and high-resolution spaceborne InSAR

An image co-registration method for wide-swath and high-reso...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Wang, Ping Li, Zhenfang Suo, Zhiyong Liu, Yanyang National Key Laboratory of Science and Technology on Radar Signal Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper, we analyse the issue of SAR image co-registration for wide-swath and high-resolution spaceborne InSAR. A solution to this problem is proposed in which three processing procedures are adopted. First, image co-registration pre-processing is performed. There are two methods for this step: one is based on InSAR orbit and radar system parameters, and the other is based on the cross-correlation information of the master and the slave image. Second, coarse co-registration is implemented by block processing. Finally, fine co-registration is executed in order to obtain sub-pixel co-registration accuracy. Data simulation and qualitative analysis of coherence show that this method can provide accurate co-registration for wide-swath and high-resolution spaceborne InSAR, and meet the practical engineering requirement. ©2009 IEEE.

关键词： Synthetic aperture radar

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Characterization of 3-D coronary tree motion from MSCT angiography

Characterization of 3-D coronary tree motion from MSCT angio...

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作者： Yang, Guanyu Zhou, Jian Boulmier, Dominique Garcia, Marie-Paule Luo, Limin Toumoulin, Christine Laboratoire Traitement du Signal et de l'Image Institut National de la Santé et de la Recherche Médicale U642 Université de Rennes 1 Rennes 35042 France Division of Image Processing Leiden University Medical Center Leiden 2300 RC Netherlands Department of Biomedical Engineering University of California Davis CA 95616 United States Département de Cardiologie CHU Rennes Rennes 35033 France Laboratory of Image Science and Technology Southeast University Nanjing 210096 China Centre de Recherche en Information Biomédicale Sino-français Université de Rennes 1 Rennes 35042 France

This paper describes a method for the characterization of coronary artery motion using multislice computed tomography (MSCT) volume sequences. Coronary trees are first extracted by a spatial vessel tracking method in each volume of MSCT sequence. A point-based matching algorithm, with feature landmarks constraint, is then applied to match the 3-D extracted centerlines between two consecutive instants over a complete cardiac cycle. The transformation functions and correspondence matrices are estimated simultaneously, and allow deformable fitting of the vessels over the volume series. Either point-based or branch-based motion features can be derived. Experiments have been conducted in order to evaluate the performance of the method with a matching error analysis. © 2009 IEEE.

关键词： Linear transformations

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Spectral analysis for emotion recognition by NMF features

Spectral analysis for emotion recognition by NMF features

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5th International Conference on Natural Computation, ICNC 2009

作者： Jeong, Kyungjoong Song, Jaiyoun Jeong, Hong Pohang University of Science and Technology Advanced Signal Processing Laboratory 790-784Pohang Korea Republic of

ISBN: (纸本)9780769537368

In this paper, we propose a new algorithm, which is using NMF features and which is different from traditional algorithms using temporal variations of the spectrogram. The proposed algorithm has been tested in several different ways by varying NMF and the speech database. The experimental results show performance of 90% and classification success of 77% for speaker dependent and independent cases, respectively. © 2009 IEEE.

关键词： Spectrum analysis

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