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IEEE Global Conference on signal and Information processing

作者： Tariq Alshawi Zhiling Long Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA

ISBN: (纸本)9781479975921

Video saliency detection is typically performed by combining temporal saliency and spatial saliency, which are detected separately. Among various available techniques, uncertainty-based combination is a unique and promising approach. In this paper, we study the uncertainty of each point within a map generated from video saliency detection. We develop an adaptive temporal correlation-based method for unsupervised uncertainty estimation. To evaluate the performance of our algorithm, we propose a systematic evaluation scheme that involves both the creation of ground truth uncertainty data and the comparison of uncertainty estimation results against the ground truth. Our experiments on the public CRCNS database show that the unsupervised uncertainty analysis algorithm is very promising.

关键词： unsupervised uncertainty analysis video saliency detection performance evaluation

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A COMPARATIVE STUDY OF QUALITY AND CONTENT-BASED SPATIAL POOLING STRATEGIES IN image QUALITY ASSESSMENT

A COMPARATIVE STUDY OF QUALITY AND CONTENT-BASED SPATIAL POO...

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IEEE Global Conference on signal and Information processing

作者： Dogancan Temel Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA

ISBN: (纸本)9781479975921

The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research interest in designing the quality features but pooling is usually overlooked compared to feature design. In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although features are the key in any image quality assessment, pooling also matters. We also propose a quality-based spatial pooling strategy that is based on linearly weighted percentile pooling (WPP). Pooling strategies are analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE and TID2013 image databases.

关键词： image quality assessment quality/distortion maps spatial pooling statistical significance

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Methods for Evaluating Medical Tests and Biomarkers

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Diagnostic and Prognostic Research 2017年第1期1卷 1-34页

作者： Gowri Gopalakrishna Miranda Langendam Patrick Bossuyt Mariska Leeflang Rob Scholten Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Marie Westwood Jos Kleinen Richard Riley Gary Collins Hans Reitsma Karel Moons Antonia Zapf Katharina Kramer Annika Hoyer Oliver Kuss J. Ensor R. D. Riley J. J. Deeks E. C. Martin Gerta Rücker Martin Schumacher Susanne Steinhauser Joie Ensor Kym Snell Brian Willis Jon Deeks Thomas Debray Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A. Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Sabine Van Huffel Jan Verbakel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O’Sullivan Rafael Perera Brian Nicholson Hannah L. Bromley Tracy E. Roberts Adele Francis Denniis Petrie G. Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G. Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Peter Selby Doug Altman William Rosenberg Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Johannes Reitsma Anne Rutjes Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Bavo De Cock Daan Nieboer Ewout W. Steyerberg Micael J. Pencina Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Zoe Plummer Barnaby Reeves Chris Rogers Andrew Mumford Kurtis Lee Veerle Verheyden Gianni D. Angelini Gavin J. Murphy Jeremy Huddy Melody Ni George Hanna Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands Department of Clinical Epidemiology and Biostatistics Academic Medical Center University of Amsterdam Amsterdam Netherlands Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK EPPI-Centre Department of Social Science University College London London UK Division of Health and Social Care Research King’s College London London UK College of Computer and Information Science Northeastern University Boston USA University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK Institute of Applied Health Research University of Birmingham Birmingham UK School of Social and Community Medicine University of Bristol Bristol UK Kleijnen Systematic Reviews Ltd York UK Research Institute for Primary Care and Health Sciences Keele University Keele UK Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Cochrane Netherlands University Medical Center Utrecht Utrecht The Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Institute for Biometry and Epidemiology German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf Germany Manchester Pharmacy School University of Manchester Manchester UK Institute for Medical Biometry and Statistics Faculty of Medicine and Medical Center – University of Freiburg Stefan-Meier-Str. 26 79104 Freiburg Germany

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Expectation propagation approach to joint channel estimation and decoding for OFDM systems

Expectation propagation approach to joint channel estimation...

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2014 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2014

作者： Wu, Sheng Kuang, Linling Ni, Zuyao Lu, Jianhua Huang, Defeng David Guo, Qinghua Department of Electronic Engineering Tsinghua University China Tsinghua Space Center Tsinghua University China Research Institute of Information Technology Tsinghua University China Shenzhen Key Laboratory of Wireless Broadband Communication and Signal Processing China School of Electrical Electronic and Computer Engineering University of Western Australia Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia

ISBN: (纸本)9781479928927

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K-A- + K log2 K), where -A- denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound. © 2014 IEEE.

关键词： Orthogonal frequency division multiplexing

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Expectation propagation based iterative group wise detection for large-scale multiuser MIMO-OFDM systems

Expectation propagation based iterative group wise detection...

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2014 IEEE Wireless Communications and Networking Conference, WCNC 2014

作者： Wu, Sheng Kuang, Linling Ni, Zuyao Lu, Jianhua Huang, Defeng David Guo, Qinghua Department of Electronic Engineering Tsinghua University Beijing China Tsinghua Space Center Tsinghua University Beijing China Research Institute of Information Technology Tsinghua University Beijing China Shenzhen Key Laboratory of Wireless Broadband Communication and Signal Processing Shenzhen China School of Electrical Electronic and Computer Engineering University of Western Australia Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia

ISBN: (纸本)9781479930838

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms. © 2014 IEEE.

关键词： Message passing

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A Performance Study of Spatial Modulation Systems Under Vehicle-to-Vehicle Channel Models

A Performance Study of Spatial Modulation Systems Under Vehi...

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IEEE Vehicular Technology Conference

作者： Yu Fu Cheng-Xiang Wang Raed Mesleh Xiang Cheng Harald Haas Yejun He Joint Research Institute for Signal and Image Processing Heriot-Watt University School of Information Science and Engineering Shandong University Electrical Engineering Department and SNCS Research Center University of Tabuk School of Electronics Engineering & Computer Science Peking University Joint Research Institute for Signal and Image Processing University of Edinburgh College of Information Engineering Shenzhen University

ISBN: (纸本)9781479944811

Spatial modulation (SM) is a relatively new multiple-input multiple-output (MIMO) technology that can provide high data rate with reasonable spectral efficiency. In this paper, the bit error rate (BER) performance of SM systems under vehicle-to-vehicle (V2V) channel models is investigated. The theoretical BER expression is given. The impact of some V2V channel model parameters on the underlying space-time correlation function (STCF) and the BER performance of SM systems are also studied. Simulation results indicate that modulation schemes, maximum Doppler frequency, the distance between the transmitter (Tx) and receiver (Rx), and antenna element spacings can affect the performance of SM systems.

关键词： Spatial modulation Vehicle-to-vehicle channel model Bit error rate Space-time correlation function

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A comparative study of computational aesthetics

A comparative study of computational aesthetics

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IEEE International Conference on image processing

作者： Dogancan Temel Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more appealing or beautiful. In order to quantify the aesthetics of an image, we need to take it one step further and model the perception of aesthetics. In this paper, we examine computational aesthetics models that use hand-crafted, generic and hybrid descriptors. We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features. However, neither generic nor hand-crafted features is sufficient to model aesthetics when we only use global features without considering spatial composition or distribution. We also follow a visual dictionary approach similar to state of the art methods and show that it performs poorly without the spatial pyramid step.

关键词： image color analysis Feature extraction Accuracy Visualization Measurement Dictionaries Training

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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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A Comparison of x-lets in Denoising cDNA Microarray images

A Comparison of x-lets in Denoising cDNA Microarray Images

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IEEE International Conference on Acoustics, Speech and signal processing

作者： Rouzbeh Shams Hossein Rabbani Saeed Gazor Electrical & Computer Engineering Department Isfahan University of Technology Isfahan Iran Biomedical Engineering Department Medical Image & Signal Processing Research Center Isfahan University of Medical Science Isfahan 81745319 Iran Electrical & Computer Engineering Department Queen's University Kingston Canada

ISBN: (纸本)9781479928941

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

关键词： microarray denoising x-let transform wavelet Noise reduction Transform Microarray wavelets Standard Widget Toolkit Electric tools images SHIELDED PAIR global thresholding Local threshold

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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