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Volume-based Magnetic Resonance Brain Image Classification

Volume-based Magnetic Resonance Brain Image Classification

引用

International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University 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 MD USA

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.

关键词： Training Support vector machine classification Image classification Vectors Volume measurement Magnetic resonance

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A Bayesian Perspective on Sparse Regularization for STAP Post-Processing

A Bayesian Perspective on Sparse Regularization for STAP Pos...

引用

IEEE Radar Conference

作者： Jason T. Parker Lee C. Potter Radar Signal Processing Branch Sensors Directorate US Air Force Research Laboratory Department of Electrical and Computer Engineering The Ohio State University

ISBN: (纸本)9781424458110

Traditional Space Time Adaptive Processing (STAP) formulations cast the problem as a detection task which results in an optimal decision statistic for a single target in colored Gaussian noise. In the present work, inspired by recent theoretical and algorithmic advances in the field known as compressed sensing, we impose a Laplacian prior on the targets themselves which encourages sparsity in the resulting reconstruction of the angle/Doppler plane. By casting the problem in a Bayesian framework, it becomes readily apparent that sparse regularization can be applied as a post-processing step after the use of a traditional STAP algorithm for clutter estimation. Simulation results demonstrate that this approach allows closely spaced targets to be more easily distinguished.

关键词： Bayesian inference Space time adaptive processing Targets Gaussian noise Compressed sensing

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OFDM signal Type Recognition and Adaptability Effects in Cognitive Radio Networks

OFDM Signal Type Recognition and Adaptability Effects in Cog...

引用

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Anna Vizziello Ian F. Akyildiz Ramon Augusti Lorenzo Favalli Pietro Savazzi Broadband Wireless Networking Laboratory School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Department of Electronics University of Pavia Pavia Italy Broadband Wireless Networking Laboratory Georgia Institute of Technology Atlanta Georgia USA Department of Signal Theory and Communications (TSC) Technical University of Catalonia (UPC) Barcelona Spain Universitat Politecnica de Catalunya Institut de Tecniques Energetiques Barcelona Catalunya ES

The ability of adapting to the environment in the most efficient way is a crucial issue in Cognitive Radio (CR) networks. For this purpose, an accurate estimation of the characteristics and activity of the Primary Users (PUs) is required. A system that takes into account heterogeneous PUs with several features is developed. A new scheme is integrated in the system to exploit these motleys and to improve the adaptability in CR networks. Through the proposed PU signal type recognition, the PU signal is detected and classified. The features of each PU type: the allowed interference levels, the bandwidth and the idle time, are extracted and exploited for CR adaptability effects. For this, a new CR throughput/interference adapter is proposed. The CR throughput is efficiently increased depending on the specific characteristics of PU types. Simulation results show that the proposed PU type recognition detects, distinguishes and classifies PU signals in Additive White Gaussian Noise (AWGN). It is shown that CR throughput varies with PU features for the improvement of CR adaptability.

关键词： Interference Throughput Feature extraction OFDM Detectors Bandwidth

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Iterative Design of MIMO Radar Transmit Waveforms and Receive Filter Bank

Iterative Design of MIMO Radar Transmit Waveforms and Receiv...

引用

IEEE International Conference on Acoustics, Speech, and signal Processing

作者： Y.-C. Wang Hongwei Liu Z.-Q. Luo State Key Laboratory on Integrated Service Networks Xidian University Xi'an 710071 China. Currently Y.-C Wang is a post doctorial fellow at the University of Minnesota National Lab of Radar Signal Processing Xidian University Xi'an 710071 China Department of Electrical Computer Science Engineering University of Minnesota Minneapolis 55455 USA

ISBN: (纸本)9781424442959

In this paper, we propose an iterative design approach to jointly optimize probing signal waveforms and a receive filter bank for a multiple-input multiple-output (MIMO) radar under a constant modulus constraint. The design goals are to approximate a desired beampattern and to minimize the auto/cross- correlation levels of the probing signal waveforms for different time lags and between different spatial angles. Since the overall design problem is nonconvex, we propose to optimize the transmit probing signals and receive filter bank separately and alternately. The optimization of receive filter bank is a standard least squares problem, while the optimization of the constant modulus transmit signal waveforms is a norm-constrained least squares problem which can be approximately solved using a low-rank semidefinite relaxation procedure. We demonstrate the effectiveness of our proposed approach through a simulation example.

关键词： Multiple-input multiple-output (MIMO) radar Beampattern Mismatched filter bank Constant modulus probing signal Auto correlation peak sidelobes Cross correlation Level signal waveform Filter banks design target Banks MIMO radar TRANSMITTING Exploration with a probe Iterative design problems

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A comparative study of hierarchical matching algorithms for face recognition

A comparative study of hierarchical matching algorithms for ...

引用

11th IAPR Conference on Machine Vision Applications, MVA 2009

作者： Momeni, Hajar Sadeghi, Mohammad T. Abutalebi, Hamid R. Signal Processing Research Laboratory Department of Electrical and Computer Engineering Yazd University Yazd Iran

ISBN: (纸本)9784901122092

The similarity measure based nearest neighbour classifier is commonly used in object recognition or retrieval systems. The result of a query in such a system is in general the images from the database whose descriptors are closest to the descriptor of the query image. An important issue in large scale recognition systems is the computational burden caused by measuring the similarity of the features derived from the query image with the templates of the database objects. Hierarchical Agglomerative Clustering (HAC) algorithms are considered as a solution to this problem. In these algorithms, a dendrogram is formed down to top in the training stage. In the recognition phase, the query image moves on the dendrogram from the highest level to lower levels in order to find the best matched object. One of the most affecting issues in these algorithms is the strategy used for building the dendrogram. In this paper, different techniques adopted for this purpose are studied and compared within the framework of a face recognition system. Our experimental results demonstrate that using an appropriate merging technique, the average recognition time reduces while the performance of the system is not highly degraded.

关键词： Face recognition

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Surveillance of pedestrian bridge traffic using neural networks

Surveillance of pedestrian bridge traffic using neural netwo...

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Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2009

作者： Watkins, Steve E. Stanley, R. Joe Gopal, Anand Moss, Randy H. Department of Electrical and Computer Engineering Applied Optics Laboratory Missouri University of Science and Technology Rolla MO 65409 United States Department of Electrical and Computer Engineering Signal Processing Laboratory Missouri University of Science and Technology Rolla MO 65409 United States

ISBN: (纸本)9780819475527

A computer-vision monitoring system is demonstrated that automatically detects the presence and location of people. The approach investigated the potential for real-time, automated surveillance and tracking in a realistic environment. Economy was obtained by the use of gray-scale, fixed perspective images and efficiency was obtained by the use of selected object features and a neural-network-processing algorithm. The system was applied to pedestrian traffic on an outdoor bridge and consequently had to handle complex images. Image sequences of single and multiple people were used with differences in clothing, position, lighting, season, etc. A two-stage algorithm was implemented in which (1) new objects were identified in a highly variable scene and (2) the objects were classified with a back-propagation neural network. The image processing techniques included segmentation and filtering and the neural network used fourteen object features as inputs. The implementation had excellent people-discrimination accuracy despite the noise in the images and had low computational complexity with respect to alternative techniques. © 2009 SPIE.

关键词： Neural networks

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A robust face detection method using intrinsic images and an energy minimization technique under the near-IR light spectrum

A robust face detection method using intrinsic images and an...

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2009 International Conference on Image Processing, computer Vision, and Pattern Recognition, IPCV 2009

作者： Lahlou, Mouncef Adjouadi, Malek Barreto, Armando Center for Advanced Technology and Education Department of Electrical and Computer Engineering Florida International University Miami FL United States Digital Signal Processing Laboratory Department of Electrical and Computer Engineering Florida International University Miami FL United States

ISBN: (纸本)9781601321190

Detecting faces accurately under natural lighting conditions remains a challenging task for advancing the performance of face recognition systems. Most face detection algorithms are associated with visible spectrum imagery which compromises their efficiency when image acquisition is subject to variable illumination. In this study, we propose a simple yet powerful solution that uses multiple cues to detect and locate faces accurately in near infrared (NIR) imaging systems. Specific objectives include (I) extract optimally the illumination and reflectance components of the original image using an intrinsic images decomposition technique, (2) modify the segmentation process by using an energy minimization procedure in an adaptive fashion, and (3) propose an enhanced face detection process on the basis of pupil localization. The evaluation of our face detector on image sequences of 45 different subjects from the CBSR NIR face dataset demonstrated a highly competitive accuracy with less than 5% error rate.

关键词： Face recognition

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Amplification of signal features using variance fractal dimension trajectory

Amplification of signal features using variance fractal dime...

引用

IEEE International Conference on Cognitive Informatics

作者： Witold Kinsner Warren Grieder Laboratory Mathematical Sciences Research Institute USA Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper describes how the selection of parameters for the variance fractal dimension (VFD) multiscale time-domain algorithm can create an amplification of the fractal dimension trajectory that is obtained for a natural-speech waveform in the presence of ambient noise. The technique is based on the variance fractal dimension trajectory (VFDT) algorithm that is used not only to detect the external boundaries of an utterance, but also its internal pauses representing the unvoiced speech. The VFDT algorithm can also amplify internal features of phonemes. This fractal feature amplification is accomplished when the time increments are selected in a dyadic manner rather than selecting the increments in a unit distance sequence. These amplified trajectories for different phonemes are more distinct, thus providing a better characterization of the individual segments in the speech signal. This approach is superior to other energy-based boundary-detection techniques. These observations are based on extensive experimental results on speech utterances digitized at 44.1 kilosamples per second, with 16 bits in each sample.

关键词： Fractals Speech analysis Pattern matching signal processing Magnetic materials Automatic speech recognition Natural languages Feature extraction Speech recognition Linear predictive coding

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Hyperspectral information compression

Hyperspectral information compression

引用

Satellite Data compression, Communication, and Processing V

作者： Chang, Chein-I 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)9780819477453

This paper investigates a new concept, called Hyperpsectral Information compression (HIC) as opposed to Hyperspectral Data compression (HDC) commonly used in the literature. A key feature that differentiates the HIC from the HDC is its focus on compression of information rather than data size. Such information compression is completely determined by a specific application in data exploitation. Accordingly, the HIC can be also referred to as exploitationbased compression. In order to substantiate the utility of the HIC, experiments are conducted to demonstrate that the HIC is indeed more effective than traditional HDC. © 2009 SPIE.

关键词： Data compression

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Design and analysis of real-time endmember extraction algorithms for hyperspectral imagery

Design and analysis of real-time endmember extraction algori...

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Satellite Data compression, Communication, and Processing V

作者： Chang, Chein-I Wu, Chao-Cheng 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)9780819477453

Endmember extraction has recently received considerable attention in hyperspectral data exploitation since they represent crucial and vital information for hyperspectral data analysis. So far, no work has been reported on how to implement endmember extraction algorithms in real-time. An endmember is defined as an idealized signature and may or may not exist as a data sample or an image pixel. The interest of endmember extraction arises in the use of hundreds of contiguous spectral channels that allows a hyperspectral imaging sensor to uncover many subtle substances in diagnostic bands. However, finding such substances also presents a great challenge to hyperspectral data analysts and becomes imperative when it comes to satellite communication if a hyperspectral imaging sensor is operated in space platform where bandwidths used by satellite links may be very limited and downloading all the data may not be realistic in many practical applications. In order to address this need many endmember extraction algorithms have been developed and designed in the past, but no work has been reported on how to implement endmember extraction algorithms in real-time. This paper investigates this issue in designing algorithms for real time processing of endmember extraction and developed several endmember extraction algorithms derived from the widely used N-finder algorithm (NFINDR) that can be implemented in real time. © 2009 SPIE.

关键词： Space platforms

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