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IEEE International Symposium on Intelligent Signal processing and Communication Systems (ISPACS)

作者： Nattachai Watcharapinchai Supavadee Aramvith Supakorn Siddhichai Sanparith Marukatat Digital Signal Processing Research Laboratory Department of Electrical Engineering Chulalongkom University Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand

The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality reduction of SIFT using principal component analysis (PCA) on each object category to reduce computational complexity and memory requirement during training process. Experimental results show that our proposed technique can reduce the dimension of SIFT up to around 80% with the same average precision compared to baseline technique without our proposed method.

关键词： Principal component analysis computer vision Histograms Digital signal processing Computational complexity Video compression Testing Training data Vocabulary Feature extraction

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Achieving Close-Capacity Performance with Simple Concatenation Scheme on Multiple-Antenna Channels

Achieving Close-Capacity Performance with Simple Concatenati...

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GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

作者： Nghi H. Tran Tho Le-Ngoc Tad Matsumoto Ha H. Nguyen Department of Electrical & Computer Engineering McGill University Montreal QUE Canada Information Theory and Signal Processing Laboratory School of Information Science Japan Advanced Institute of Science and Technology Japan Department of Electrical & Computer Engineering University of Saskatchewan Saskatoon SAS Canada

This paper proposes a simple yet capacity-approaching concatenation of a mixture of short memory length convolutional codes and simple rate-1 block code followed by either complex 1-dimensional (1-D) anti-Gray or Gray mapping over multiple antenna channels with quadrature phase-shift keying (QPSK). By interpreting rate-1 code together with 1-D mapping as a multi-D mapping employed over multiple transmit antennas, the error performance is analyzed in two regions, the error-floor and turbo pinch-off regions. In the former one, a tight union bound and design criterion on the asymptotic performance are first derived, which provide an useful tool to predict the error performance. Based on the design criterion, an optimal rate-1 code for each 1-D mapping is then constructed to achieve the best asymptotic performance. In the turbo pinch-off area, by using extrinsic information transfer (EXIT) chart, the most suitable mixed codes are selected for both symmetric and asymmetric antenna setups. It is demonstrated that the simple concatenation scheme can achieve near-capacity. Furthermore, its error performance is comparable to that obtained by using well-designed irregular low-density parity-check (LDPC) and repeat accumulate (RA) codes, and thereby, outperforms a scheme employing a parallel concatenated turbo code.

关键词： Parity check codes Convolutional codes Block codes Phase shift keying Quadrature phase shift keying Transmitting antennas Error analysis Performance analysis Concatenated codes Turbo codes

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Brain tissue classification using independent vector analysis (IVA) for magnetic resonance image

Brain tissue classification using independent vector analysi...

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2009 9th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2009

作者： Chiou, Yaw-Jiunn Chen, Hsiang-Min Chai, Jyh Wen Chen, Clayton Chi-Chang Ouyang, Yen-Chieh Su, Wu-Chung Yang, Ching-Wen Lee, San-Kan Chang, Chein-I. Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology College of Medicine China Medical University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Computer Center Taichung Veterans General Hospital Taichung 407 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)9780769536569

The purpose of this study is to present a new method, independent vector analysis (IVA), by extending independent component analysis (ICA) of univariate source signals to multivariate source signals on Magnetic Resonance Imaging (MRI). IVA is utilized to relief the limitation of the conventional ICA approach. The proposed method can resolve the permutation problem during individual ICA runs for group brain MR images. The proposed IVA method in conjunction with support vector machine (SVM), we can effectively separate the different part of gray, white matter and cerebrospinal fluid (CSF) from brain soft tissues. In order to demonstrate the proposed IVA-SVM method, experiments are conducted for performance analysis and evaluation. Simulation results show that using IVA can greatly release from the problem cause from traditional ICA to the situation of analyzing inconsistent results of MR image. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Neuro-fuzzy based fault diagnosis of a Steam Generator

Neuro-fuzzy based fault diagnosis of a Steam Generator

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7th IFAC International Symposium on Fault Detection, Supervision and Safety of Technical Systems, SAFEPROCESS'09

作者： Razavi-Far, Roozbeh Davilu, Hadi Palade, Vasile Lucas, Caro Department of Nuclear Engineering Amirkabir University of Technology P.O. Box 15875 4413 Tehran Iran Computing Laboratory Oxford University Wolfson Building Parks Road Oxford OX1 3QD United Kingdom Center of Excellence on Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran P.O. Box 14395 1515 Tehran Iran

ISBN: (纸本)9783902661463

This paper focuses on the development and application of a Neuro-Fuzzy (NF) networks-based scheme for Fault Detection and Isolation (FDI) in a U-tube Steam Generator (UTSG). First, a NF network is trained with data collected from a full scale UTSG simulator, and residuals are generated for fault detection. To identify the UTSG, a Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained using the Locally Linear Model Tree (LOLIMOT) algorithm which is an incremental tree structure algorithm. Then, an evolutionary algorithm is used to train a Mamdani type NF network to classify the residuals. The residuals are analyzed by using this NF classifier for fault isolation purposes. © 2009 IFAC.

关键词： Fault detection

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Fingerprint Feature Compression Using Statistical Coding Techniques

Fingerprint Feature Compression Using Statistical Coding Tec...

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IEEE India Conference (INDICON)

作者： C. Saravanan Sanjeev S. Malalur Micahel T. Manry Computer Centre National Institute of Technology Durgapur West Bengal India Department of Electrical Engineering Image Processing & Neural Networks Laboratory University of Technology Arlington USA

ISBN: (纸本)9781424448586;9781424448593

We have proposed a new fingerprint feature compression scheme, which extracts the fingerprint feature using pseudo-spectral fusion approach and compresses the fingerprint feature using statistical coding technique. The compression scheme starts with fingerprint image acquisition, enhancement, and normalization. Further, the direction and density of the fingerprint image are extracted. Fusing these two direction and density data results finger print feature data. The fingerprint feature data is applied directly using a statistical coding technique to get compressed data. The Huffman coding and arithmetic coding is used for compression and found arithmetic coding performs better. This proposed compression scheme achieves nearly 85% compression rate, which is higher compared to other present techniques achieved 45% to 63% compression.

关键词： Fingerprint recognition Image coding Image matching Multiresolution analysis Arithmetic Neural networks Data mining Feature extraction Huffman coding Discrete wavelet transforms

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Interest point detection for hyperspectral imagery

Interest point detection for hyperspectral imagery

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Roysam, Badrinath Mukherjee, Amit Center for Subsurface Sensing and Imaging Systems Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico at Mayaguez Mayaguez PR.00681-9048 United States Center for Subsurface Sensing and Imaging Systems Department of Electrical Computer and Systems Engineering Rensseleaer Polytechnic Institute Troy NY. 12180-3590 United States

ISBN: (纸本)9780819476005

This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature. © 2009 SPIE.

关键词： Object detection

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An automatic computer-aided detection system for meniscal tears on magnetic resonance images

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IEEE Transactions on Medical Imaging 2009年第8期28卷 1308-1316页

作者： Ramakrishna, Bharath Liu, Weimin Saiprasad, Ganesh Safdar, Nabile Chang, Chein-I Siddiqui, Khan Kim, W. Siegel, Eliot Chai, Jyh-Wen Chen, Clayton Chi-Chang Lee, San-Kan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD 21250 United States University of Maryland School of Medicine Baltimore MD 21201 United States Department of Electrical Engineering and Environmental Restoration Disaster Reduction Research Center National Chung Hsing University Taichung 402 Taiwan VA Maryland Health Care System Baltimore MD 21201 United States Hospital of the University of Pennsylvania Philadelphia PA 19104 United States Department of Radiology College of Medicine China Medical University Taichung 404 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 40705 Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung 406 Taiwan Department of Radiology Taichung Veterans General Hospital Taichung 407 Taiwan

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee. © 2009 IEEE.

关键词： Magnetic resonance imaging

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Editorial: Game theory in signal processing and communications

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Eurasip Journal on Advances in Signal processing 2009年 2009卷

作者： Jorswieck, Eduard A. Boche, Holger Han, Zhu Larsson, Erik G. Department of Mobile Communications School of Electrical Engineering and Computer Sciences Technical University of Berlin Berlin Germany Wireless Networking Signal Processing and Security Lab Department of Electrical and Computer Engineering University of Houston Houston TX 77004 United States Linkping University SE-581 83 Linkping Sweden Communications Laboratory Faculty of Electrical Engineering and Information Technology Dresden University of Technology 01062 Dresden Germany

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Constant-modulus partially correlated signal design for uniform linear and rectangular MIMO radar arrays

Constant-modulus partially correlated signal design for unif...

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International Waveform Diversity and Design Conference

作者： Daniel R. Fuhrmann J. Paul Browning Muralidhar Rangaswamy Department of Electrical and Computer Engineering Michigan Technological University Houghton MI USA Radar Signal Processing Branch U.S. Air Force Research Laboratory OH USA

A method for generating partially correlated signals that result in arbitrary rectangular transmit beampatterns in a MIMO radar system is described. These signals are computed using frequency-offset complex exponentials (1-D) or Kronecker products of frequency-offset complex exponentials (2-D), followed by pointwise multiplication of the transmit signal vectors by a scalar pseudo-noise spreading sequence. Except for the spreading sequence, which can be pre-computed, the signals are given in closed form and thus require no numerical optimization. The transmit beampattern shape is controlled by one scalar parameter in 1-D and two scalar parameters in 2-D. The transmit beampattern is the convolution of a rectangle with a sincsquared function in 1-D, and the separable product of such functions in 2-D. The signals are constant-modulus, have desirable temporal autocorrelation properties, and are simple to compute.

关键词： Signal design MIMO Signal generators Phased arrays Radar signal processing Military computing Frequency Shape control Force sensors Laboratories

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Gait Recognition Using Wavelet Packet Silhouette Representation and Transductive Support Vector Machines

Gait Recognition Using Wavelet Packet Silhouette Representat...

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Congress on Image and Signal processing, CISP

作者： Farzin Dadashi Babak N. Araabi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Hospital Detroit MI USA

Gait is an idiosyncratic biometric that can be used for human identification at a distance and as a result gained growing interest in intelligent visual surveillance. In this paper, an efficient gait recognition method based on describing subject outer body contour deformations using wavelet packets is proposed. With the use of matching pursuit algorithm, k bases of wavelet packet tree that have maximum similarity to the signal are selected and corresponding coefficients are used as features. Finally, transductive support vector machine (TSVM) classification is utilized on computed eigengait space for semi-supervised identification. The proposed method of selecting features which uses a complete orthogonal or near orthogonal basis from a wavelet packet library of bases and investigating the correlational structure of gait features for each individual using TSVM, result in encouraging identification performance.

关键词： Wavelet packets Support vector machines Matching pursuit algorithms Biometrics Humans Machine intelligence Surveillance Pursuit algorithms Support vector machine classification Libraries

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