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IEEE International Symposium on Biomedical Imaging

作者： Dongqing Chen Rachid Fahmi Aly A. Farag Robert L. Falk Gerald W. Dryden Computer Vision & Image Processing (CVIP) Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Medical Imaging Jewish Hospital and Saint Marys Healthcare Louisville KY USA Division of Gastroenterology/Hepatology Department of Medicine University of Louisville Louisville KY USA

ISBN: (纸本)9781424439317

This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The proposed method has been tested on 22 real CT colonography datasets with various pathologies, and the segmentation accuracy has achieved 98.40%.

关键词： Colonography Colon Virtual colonoscopy Cancer Image segmentation Colonic polyps Level set Deformable models Computed tomography Visualization

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Development and Evaluation of Text Localization Techniques Based on Structural Texture Features and Neural Classifiers

Development and Evaluation of Text Localization Techniques B...

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International Conference on Document Analysis and Recognition

作者： Christos Emmanouilidis Costas Batsalas Nikos Papamarkos Computational Systems Unit CETI/R. C. ATHENA Xanthi Greece Image Processing and Multimedia Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Democritus University of Thrace Komotini GR

ISBN: (纸本)9781424445004

This paper presents a text localization approach for binarized printed document images. Emphasis is given to the feature extraction and feature selection stages. In the former, several document structure elements and spatial features, likely to convey useful information, are extracted. In the latter, evolutionary multi-objective feature selection is employed to identify combinations of features with simultaneous good performance in terms of text localization sensitivity and specificity. The selected features are applied to a range of classifiers. Performance results over document image sets from known databases are presented, employing the classifiers with or without feature selection. The results suggest that the hybrid techniques, which utilize the classifiers in combination with the customized pre-processing, feature extraction and feature selection stages, exhibit promising performance on a range of document images.

关键词： Feature extraction Data mining Image segmentation Text analysis Image analysis Image texture analysis Sensitivity and specificity Support vector machines Support vector machine classification Image recognition

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Comparative evaluation of spatial context techniques for semantic image analysis

Comparative evaluation of spatial context techniques for sem...

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International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS

作者： G. Th. Papadopoulos C. Saathoff M. Grzegorzek V. Mezaris I. Kompatsiaris S. Staab M. G. Strintzis Centre of Research & Technology Hellas Informatics and Telematics Institute Thessaloniki Greece Information Processing Laboratory Electrical & Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece Information Systems & Semantic Web Research Group Institute of Computer Science University of Koblenz Koblenz Germany

In this paper, two approaches to utilizing contextual information in semantic image analysis are presented and comparatively evaluated. Both approaches make use of spatial context in the form of fuzzy directional relations. The first one is based on a Genetic Algorithm (GA), which is employed in order to decide upon the optimal semantic image interpretation by treating semantic image analysis as a global optimization problem. On the other hand, the second method follows a Binary Integer Programming (BIP) technique for estimating the optimal solution. Both spatial context techniques are evaluated with several different combinations of classifiers and low-level features, in order to demonstrate the improvements attained using spatial context in a number of different image analysis schemes.

关键词： Image analysis Image segmentation Classification algorithms Information processing Image generation Image retrieval Informatics Telematics Information systems Semantic Web

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Joint user scheduling and beamforming for underlay cognitive radio systems

Joint user scheduling and beamforming for underlay cognitive...

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Asia-Pacific Conference on Communications

作者： Wenhui Zong Sen Shao Qingmin Meng Weiping Zhu Institute of Signal and Transmission Nanjing University of Posts and Telecommunications Nanjing China National Mobile Communications Research Laboratory South-East University Nanjing China Centre for Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

ISBN: (纸本)9781424447848

In this paper, we consider underlay cognitive radio systems in which secondary users coexist with users in primary networks whose radio band is licensed. The secondary subnetwork comprising the cognitive users utilizes the cognitive radio technology to make opportunistic access to the licensed spectrum that primary users own. In this scheme, we discuss the issue of joint user scheduling and beamforming in order to make sure that received interferences at the primary users maintain below an affordable threshold level. The user scheduling includes two procedures: 1) primary users scheduling; 2) secondary users scheduling. However, these two steps are carried out coordinately and the selected primary user and corresponding cognitive users can be obtained. In addition, Zero-forcing Beamforming (ZFBF) and antenna selection are integrated into our scheme where the former is used to support multiple concurrent streams transmission and the latter is used to reduce the feedback in the uplink of the secondary system. Our performance results show that the proposed scheme can significantly improve the throughput of the whole cognitive radio networks.

关键词： Array signal processing Cognitive radio Mobile communication Wireless communication Interference constraints Receiving antennas Downlink Signal processing Transmitting antennas Throughput

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A Near Fair User Scheduling Scheme in Cognitive Radio Networks

A Near Fair User Scheduling Scheme in Cognitive Radio Networ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Qingmin Meng Wenhui Zong Sen Shao Weiping Zhu Baoyu Zheng National Mobile Communications Research Laboratory South-East University Nanjing China Institute of Signal and Transmission Nanjing University of Posts and Telecommunications Nanjing China Centre of Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

Nowadays, cognitive radio technique has been acknowledged as a smart and potential method to improve the spectrum utilization. In this work, we investigate a cognitive radio communication scheme, where a number of cognitive users, each with multiple antennas, can share the spectrum with the primary user. To decrease the inter-user interference, zero-forcing beamforming (ZFBF) is considered. We presented a design of three-step selection for both the cognitive users and their antennas at receiver. In the first step of the studied scheme, antennas-selection is performed to based on a certain algorithm and then user-selection is performed based on Proportional Fair algorithm. Next the active set of cognitive users is selected according to the correlation between the users in the third step. Simulation results show that at the expense of some sum-rate loss, our proposed scheme can realize near fair resource allocation among the cognitive users as well as the decrease of the interference to the primary user and of the feedback.

关键词： Cognitive radio Receiving antennas Interference Downlink Scheduling Antenna feeds Base stations Signal processing Mobile communication Array signal processing

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NEURO-FUZZY BASED FAULT DIAGNOSIS OF A STEAM GENERATOR

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IFAC Proceedings Volumes 2009年第8期42卷 1180-1185页

作者： Roozbeh Razavi-Far Hadi Davilu Vasile Palade Caro Lucas Department of Nuclear Engineering Amirkabir University of Technology P.O. Box 15875-4413 Tehran Iran (Tel:+98-***) Computing Laboratory Oxford University Wolfson Building Parks Road Oxford OX1 3QD UK Center of Excellence on Control and Intelligent Processing Department of Electrical and Computer Engineering University of Tehran P.O. Box 14395-1515 Tehran Iran

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.

关键词： Fault Diagnosis Neuro-Fuzzy Networks Locally Linear Neuro Fuzzy Model Locally Linear Model Tree Steam Generator

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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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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

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

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.

关键词： Brain Image analysis Magnetic analysis Magnetic resonance Independent component analysis Magnetic resonance imaging Support vector machines Signal analysis Image resolution Signal resolution

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Estimation of proper parameter values for document binarization

Estimation of proper parameter values for document binarizat...

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10th IASTED International Conference on computer Graphics and Imaging, CGIM 2008

作者： Badekas, E. Papamarkos, N. Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9780889867192

Most of the existing document-binarization techniques deal with many parameters that require a priori setting of their values. Due to the unknown of the ground-truth images, the evaluation of document binarization techniques is subjective and employs human observers for the estimation of the appropriate parameter values. The selection of the appropriate values for these parameters is crucial and influences to the final binarization. However, there is no predetermined set of parameters that guarantees optimal binarization for all document images. This paper proposes a new technique that allows the estimation of proper parameters values for each one of the document binarization techniques. The proposed approach is based on a statistical performance analysis of a set of binarization results, which are obtained by applying various binarization techniques with different parameter values. The proposed statistical performance analysis can also depicts the best document binarization result obtained by a set of document binarization techniques.

关键词： Parameter estimation

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Ground truth data collection for unmixing algorithm evaluation

Ground truth data collection for unmixing algorithm evaluati...

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

作者： Rivera-Borrero, Carlos Rosario, Samuel Hunt, Shawn Zayas, Carmen Mundorf, Adrienne Cardona, Suhaily Laboratory for Applied Remote Sensing and Image Processing Electrical and Computer Engineering Department University of Puerto Rico Mayagüez Campus

ISBN: (纸本)9780819471574

This paper presents a ground truth data collection effort along with its use in evaluating unmixing algorithms. Unmixing algorithms are typically evaluated using synthetic data generated by selecting endmember spectrums and adding them in different amounts and with added noise. Going from synthetic to real data poses many problems. One of the greatest is the amount of data to be collected. Also, there will be many unmodeled variations in real data. These include greater variation of the endmembers, additional endmembers that are a very small percentage of the image, and nonlinear effects in the data that are not modeled. The data collation effort produced a high resolution class map along with spectral measurements of 153 different sampling sites to validate the map. The methodology for using this high resolution class map for generating the ground truth data for use in the unmixing algorithms is presented. Specifically, a 1m class map is used to generate the endmember abundances for every pixel in a 30m Hyperion image of the Enrique Reef in Southwest Puerto Rico. The results using two unmixing algorithms, one with a sum to one constraint and the other with a non-negative constraint are presented. The unmixing results for each endmember are presented along with a newly developed unmixing parameter called the Correct Unmixing Index (CUI).

关键词： Data acquisition

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Deciphering gunshot recordings

Deciphering gunshot recordings

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AES 33rd International Conference: Audio Forensics-Theory and Practice

作者： Maher, Robert C. Shaw, Steven R. Digital Audio Signal Processing Laboratory Department of Electrical and Computer Engineering Montana State University Bozeman MT United States

Audio gunshot recordings can be helpful for crime scene reconstruction, estimation of the shooter's location and orientation, and verification of eyewitness accounts. The audio evidence can include the muzzle blast, the shock wave signature if the projectile is traveling at supersonic speed, and possibly even the characteristic sound of the firearm's mechanical action if the recording is obtained close to the shooting position. To investigate the acoustical phenomena associated with gunshot evidence, a systematic set of rifle shots were made from distances ranging from 10 meters to nearly 800 meters away from the recording microphone. This paper summarizes the primary acoustical evidence derived from these recorded gunshots, and suggests several strengths and weaknesses of gunshot analysis for forensic purposes.

关键词： Shock waves

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