Emerging Trends in Image Processing, Computer Vision and Pattern Recognition (eBook)

Hamid R Arabnia, Leonidas Deligiannidis (Herausgeber)

eBook Download: EPUB

2014 | 1. Auflage
640 Seiten
Elsevier Science (Verlag)
978-0-12-802092-0 (ISBN)

Infos

Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors' Leonidas Deligiannidis and Hamid Arabnia cover; - Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation - Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication - Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. - How to use image processing and visualization to analyze big data. - Discusses novel applications that can benefit from image processing, computer vision and pattern recognition such as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. - Covers key application techniques in computer vision from fundamentals to mid to high level processing some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication. - Presents a number of pattern recognition algorithms and methodologies including but not limited to; supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. - Explains how to use image processing and visualization to analyze big data.

Inhalt

Front Cover 1
Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition 4
Copyright 5
Contents 6
Contributors 22
Acknowledgments 30
Preface 32
Introduction 36
Part 1: Image and signal processing 38
Chapter 1: Denoising camera data: 
40 
1. Introduction 40
2. Camera noise 41
3. Adaptive raw data denoising 43
3.1. Luminance Transformation of Bayer Data 43
3.2. LPA-ICI for Neighborhood Estimation 44
3.3. Shape-adaptive DCT and Denoising via Hard Thresholding 44
4. Experiments: Image quality vs system performance 45
4.1. Visual Quality of Denoising Results 46
4.2. Processing Real Camera Data 47
5. Video Sequences 51
5.1. Implementation Aspects 52
6. Conclusion 52
References 53
Chapter 2: An approach to classifying four-part music in multidimensional space 56
1. Introduction 56
1.1. Related Work 56
1.2. Explanation of Musical Terms 56
2. Collecting the pieces-training and test pieces 57
2.1. Downloading and Converting Files 58
2.2. Formatting the MusicXML 58
3. Parsing musicXML-training and test pieces 60
3.1. Reading in Key and Divisions 61
3.2. Reading in Notes 61
3.3. Handling Note Values 62
3.4. Results 63
4. Collecting Piece Statistics 63
4.1. Metrics 63
5. Collecting Classifier Statistics-Training Pieces Only 65
5.1. Approach 66
6. Classifying Test Pieces 66
6.1. Classification Techniques 67
6.2. User Interface 68
6.3. Classification Steps 68
6.4. Testing the Classification Techniques 69
6.5. Classifying from Among Two Composers 69
6.6. Classifying from Among Three Composers 70
6.7. Selecting the Best Metrics 70
7. Additional Composer and Metrics 71
7.1. Lowell Mason 71
7.2. Additional Metrics 73
8. Conclusions 74
References 53
Further reading 74
Chapter 3: Measuring rainbow trout by using simple statistics 76
1. Introduction 76
2. Experimental prototype 77
2.1. Canalization System 78
2.2. Illumination System 78
2.3. Vision System 79
3. Statistical Measuring Approach 79
4. Experimental framework 80
4.1. Testing Procedure 81
5. Performance evaluation 85
6. Conclusions 89
Acknowledgments 89
References 53
Chapter 4: Fringe noise removal of retinal fundus images using trimming regions 92
1. Introduction 92
1.1. Image Processing 93
1.2. Retinal Image Processing 94
1.2.1. Ophthalmological Data 94
2. Methodology 95
2.1. Implementation 97
3. Results and Discussion 99
4. Conclusion 99
References 100
Chapter 5: pSQ: Image quantizer based on contrast band-pass filtering 104
1. Introduction 104
2. Related Work: JPEG 2000 Global Visual Frequency Weighting 105
3. Perceptual quantization 105
3.1. Contrast Band-Pass Filtering 105
3.2. Forward Inverse Quantization 106
3.3. Perceptual Inverse Quantization 110
4. Experimental results 111
4.1. Based on Histogram 111
4.2. Correlation Analysis 111
5. Conclusions 115
Acknowledgments 122
References 122
Chapter 6: Rebuilding IVUS images from raw data of the RF signal exported by IVUS equipment 124
1. Introduction 124
2. Method for IVUS image reconstruction 125
2.1. RF Dataset 126
2.2. Band-Pass Filter 127
2.3. Time Gain Compensation 127
2.4. Signal Envelope 129
2.5. Log-Compression 130
2.6. Digital Development Process 130
2.7. Postprocessing 130
3. Experimental results 131
4. Discussion, conclusion, and future work 132
Acknowledgments 133
References 133
Chapter 7: XSET: Image coder based on contrast band-pass filtering 136
1. Introduction 136
2. Related Work: JPEG2000 Global Visual Frequency Weighting 137
3. Image entropy encoding: XSET algorithm 138
3.1. Perceptual Quantization 138
3.2. Startup Considerations 139
3.3. Coding Algorithm 142
4. Experiments and results 144
5. Conclusions 150
Acknowledgment 151
References 151
Chapter 8: Security surveillance applications utilizing parallel video-processing techniques in the spatial domain 154
1. Introduction 154
2. Graphical Processing Unit and Compute Unified Device Architecture 154
3. Parallel algorithms for image processing 156
4. Applications for surveillance using parallel video processing 158
4.1. Motion Detector 159
4.2. Over a Line Motion Detector 160
4.3. Line Crossing Detector 161
4.4. Area Motion Detector 164
4.5. Fire Detection 164
5. Conclusion 165
Acknowledgments 165
References 165
Chapter 9: Highlight image filter significantly improves optical character recognition on text images 168
1. Introduction 168
1.1. Properties of Highlight Image Filter 169
2. Description of smart contrast image filter 169
2.1. Contrast Image Filter 170
2.1.1. Description of the optimized implementation of contrast image filter using ``color matrix´´ technique 171
2.2. New Image Filter: Smart Contrast 172
2.3. Visual Result of Applying Smart Contrast on Images 173
3. Description of highlight image filter 174
3.1. Description of the Image Filters Visual Effects That Are Included in Highlights Visual Effect 174
3.2. New Image Filter: Highlight 175
3.3. Visual Results of Applying Highlight Filter on Images 179
3.4. Highlight Image Filter Program Code and Visual Representation 180
4. Description of the optimized implementation of smart contrast and highlight Using ``Byte Buffer´´ Techniques 180
5. Conclusions 183
Nomenclature 184
References 184
Chapter 10: A study on the relationship between depth map quality and stereoscopic image quality using upsampled depth maps 186
1. Introduction 186
2. Objective quality assessment tools 188
2.1. FR IQA Tools 189
2.1.1. Peak signal-to-noise ratio 189
2.1.2. Structural similarity index measure 189
2.1.3. Visual information fidelity 189
2.2. NR IQA Tools 190
2.2.1. Sharpness degree 190
2.2.2. Blur metric 190
2.2.3. Blind image quality index 190
2.2.4. Natural image quality evaluator 191
3. 3D Subjective Quality Assessment 191
4. Experimental results 191
5. Conclusion 196
References 196
Chapter 11: .GBbBShift: Method for introducing perceptual criteria to region of interest coding 198
1. Introduction 198
2. Related work 200
2.1. BbB Shift 200
2.2. GBbBShift 202
3. Perceptual GBbBShift 203
3.1. Quantization 203
3.2. .GBbBShift Algorithm 204
4. Experimental results 205
4.1. Application in Well-Known Test Images 205
4.2. Application in Other Image Compression Fields 216
5. Conclusions 217
Acknowledgments 218
References 218
Chapter 12: DT-Binarize: A decision tree based binarization for protein crystal images 220
1. Introduction 220
2. Background 222
2.1. Image Binarization Methods 222
2.1.1. Otsu threshold 222
2.1.2. 90th Percentile green intensity threshold (g90) 222
2.1.3. Maximum green intensity threshold (g100) 224
3. DT-Binarize: Selection of best binarization method using decision tree 225
3.1. Overview 225
3.2. Stages of the Algorithm 225
3.2.1. Median filter 225
3.2.2. Contrast stretching 226
3.2.3. Decision tree 226
3.3. Application of Dt-Binarize on Protein Crystal Images 227
4. Experiments and results 228
4.1. Dataset 228
4.1.1. 2D Plates 228
4.1.2. Small 3D crystals 228
4.1.3. Large 3D crystals 230
4.2. Correctness Measurement 230
4.3. Results 232
5. Conclusion 235
Acknowledgment 236
References 236
Chapter 13: Automatic mass segmentation method in mammograms based on improved VFC snake model 238
1. Introduction 238
2. Methodology 239
2.1. Mammogram Database 239
2.2. Mammogram Preprocessing 240
2.2.1. Label removal 240
2.2.2. Image enhancement 240
2.2.3. Morphological filter 241
2.3. ROI Extraction and Location 241
2.3.1. Edge extraction 241
2.3.2. Hough transform detection 241
2.3.3. Mass location parameter 244
2.4. Mass Segmentation 244
2.4.1. Typical VFC Snake model 244
2.4.2. Improved VFC Snake model 245
3. Experiment results and discussion 246
3.1. Experiments Results 249
3.2. Algorithm Performance Analysis 249
3.2.1. Detection rate 249
3.2.2. Segmentation accuracy 250
3.2.3. Segmentation similarity 251
4. Conclusions 252
Acknowledgments 253
References 253
Chapter 14: Correction of intensity nonuniformity in breast MR images 256
1. Introduction 256
2. Preprocessing steps 257
2.1. Noise Reduction 257
2.2. Bias Field Reduction 258
2.3. Locally Normalization Step 259
2.4. Hybrid Method for Bias Field Correction 259
2.4.1. Bias field model 260
2.4.2. Correction step 260
2.4.3. Field estimation 261
3. Experimental Results 263
4. Conclusion 264
Acknowledgments 265
References 265
Chapter 15: Traffic control by digital imaging cameras 268
1. Introduction 268
2. Paper Overview 269
3. Implementation 269
4. Traffic detectors 270
4.1. Induction Loops 270
4.2. Microwave Radar 271
4.3. Infrared Sensors 271
4.4. Video Detection 272
5. Image processing 273
5.1. Basic Types of Images 274
5.1.1. Binary image 274
5.1.2. Grayscale image 274
5.1.3. True color or RGB image 274
5.1.4. Indexed images 275
6. Project design 275
6.1. Red-Light Violation 277
6.2. Speed Violation 278
6.3. Plate Numbers Recognition 279
7. Performance analysis 280
7.1. Speed Violation 280
7.2. Red Violation 281
7.3. Plate Position Determination 281
8. General Conclusion 283
8.1. Problems 283
8.2. Future Work 283
References 283
Chapter 16: Night color image enhancement via statistical law and retinex 286
1. Introduction 286
2. Overview of Retinex Theory 287
2.1. The Basic Idea of Retinex Theory 287
2.2. The "halo effect" 287
3. Analyzing the transformation law and enhancing the nighttime image 287
4. Comparison and results 291
5. Application 296
6. The conclusion 296
References 297
Part 2: Computer vision and recognition systems 300
Chapter 17: Trajectory evaluation and behavioral scoring using JAABA in a noisy system 302
1. Introduction 302
2. Methods 303
2.1. ML in JAABA and Trajectory Scoring 305
3. Results 306
4. Discussion 310
Acknowledgments 312
References 312
Chapter 18: An algorithm for mobile vision-based localization of skewed nutrition labels that maximizes specificity 314
1. Introduction 314
2. Previous work 315
3. Skewed NL localization 316
3.1. Detection of Edges, Lines, and Corners 316
3.2. Corner Detection and Analysis 319
3.3. Selection of Boundary Lines 320
3.4. Finding Intersections in Cartesian Space 321
4. Experiments 323
4.1. Complete and Partial True Positives 323
4.2. Results 325
4.3. Limitations 326
5. Conclusions 327
References 329
Chapter 19: A rough fuzzy neural network approach for robust face detection and tracking 332
1. Introduction 332
2. Theoretical background 334
3. Face-detection method 335
3.1. The Proposed Multiscale Method 337
3.2. Clustering Subnetwork 338
4. Skin Map Segmentation 341
4.1. Skin Map Segmentation Results 341
5. Face detection 342
6. Face Tracking 343
7. Experiments 344
7.1. Face-Detection Experiments 344
7.1.1. Experiment 1 344
7.1.2. Experiment 2 346
7.2. Face-Tracking Experiments 347
7.2.1. Experiment 1 347
7.2.2. Experiment 2 348
7.2.3. Experiment 3 348
8. Conclusions and Future Works 349
Acknowledgments 349
References 350
Chapter 20: A content-based image retrieval approach based on document queries 352
1. Introduction 352
2. Related Work 353
3. Our approach 354
4. Experimental setup 359
5. Future research 364
Acknowledgments 365
References 365
Chapter 21: Optical flow-based representation for video action detection 368
1. Introduction 368
2. Related work 369
3. Temporal segment representation 371
4. Optical flow 373
4.1. Derivation of Optical Flow 374
4.2. Algorithms 374
4.2.1. Differential Techniques 375
4.2.2. Region-Based Matching 375
4.2.3. Energy-Based Methods 376
4.2.4. Phase-Based Techniques 376
5. Optical flow-based segment representation 376
5.1. Optical Flow Estimation 376
5.2. Proposed Representation 378
6. Cut Detection Inspiration 381
7. Experiments and results 382
8. Conclusion 385
References 386
Chapter 22: Anecdotes extraction from webpage context as image annotation 390
1. Introduction 390
2. Literature background 391
2.1. Automatic Image Annotation 391
2.2. Keyword Extraction 391
2.3. Lexical Chain 392
3. Research design 393
3.1. Research Model Overview 393
3.2. Chinese Lexical Chain Processing 394
3.2.1. Step 1: Build a directed graph 395
3.2.2. Step 2: Calculate average distribution rate and degree to concatenate vertices 396
3.2.3. Step 3: Run iteration 397
3.2.4. Step 4: Execute postprocessing 398
3.2.5. Term weighting 398
4. Evaluation 399
4.1. Evaluation of Primary Annotation 399
4.2. Expert Evaluation of Secondary Annotation 399
4.3. User Evaluation of Secondary Annotation 400
4.4. Results of Image Annotation 400
4.5. Performance Testing 401
5. Conclusion 402
Acknowledgments 402
References 402
Chapter 23: Automatic estimation of a resected liver region using a tumor domination ratio 406
1. Introduction 406
2. Estimating an ideal resected region using the TDR 408
3. Estimating an Optimal Resected Region Under the Practical Conditions in Surgery 411
4. Modifying a Resected Region Considering Hepatic Veins 413
5. Conclusion 414
References 415
Chapter 24: Gesture recognition in cooking video based on image features and motion features using Bayesian network class... 416
1. Introduction 416
2. Related work 418
3. Our Method 419
3.1. Our Recognition System Overview 419
3.2. Preprocessing Input Data 420
3.3. Image Feature Extraction 421
3.4. Motion Feature Extraction 422
3.5. BNs Training 422
4. Experiments 424
4.1. Dataset 424
4.2. Parameter Setting 424
4.3. Results 425
5. Conclusions 427
References 53
Chapter 25: Biometric analysis for finger vein data: Two-dimensional kernel principal component analysis 430
1. Introduction 430
2. Image Acquisition 431
3. Two-dimensional principal component analysis 432
4. Kernel mapping along row and column direction 433
4.1. Two-Dimensional KPCA 433
4.2. Kernel Mapping in Row and Column Directions and 2DPCA 434
5. Finger Vein Recognition Algorithm 435
5.1. ROI Extraction 435
5.2. Image Normalization 436
5.3. Feature Extraction and Classification Method 436
6. Experimental results on finger vein database 436
6.1. Experimental Setup-1 437
6.2. Experimental Setup-2 437
7. Conclusion 441
References 441
Chapter 26: A local feature-based facial expression recognition system from depth video 444
1. Introduction 444
2. Depth Image Preprocessing 445
3. Feature extraction 445
3.1. LDP Features 447
3.2. PCA on LDP Features 449
3.3. LDA on PCA Features 449
3.4. HMM for Expression Modeling and Recognition 450
4. Experiments and results 451
5. Concluding Remarks 454
Acknowledgments 454
References 454
Chapter 27: Automatic classification of protein crystal images 458
1. Introduction 458
2. Image Categories 459
3. System overview 460
4. Image preprocessing and feature extraction 461
4.1. Green Percentile Image Binarization 462
4.2. Region Features 463
4.3. Edge Features 463
4.4. Corner Features 465
4.5. Hough Line Features 465
5. Experimental results 465
6. Conclusion and Future Work 467
Acknowledgment 468
References 468
Chapter 28: Semi-automatic teeth segmentation in 3D models of dental casts using a hybrid methodology 470
1. Introduction 470
2. Dental Study Model 471
2.1. 3D Model Acquisition 471
3. Point cloud segmentation 472
3.1. RANSAC 473
3.2. Region Growing Segmentation 473
3.3. Min-Cut 473
3.4. Feature Sampling Using NARF 474
3.5. The Hybrid Technique 475
4. Results of segmentation techniques applied to 3D dental models 477
4.1. First, a Test Using RANSAC 477
4.2. Gum Extraction Using Region Growing 478
4.3. Per-Tooth Separation Using Min-Cut 478
4.4. Semi-Automatic Segmentation (Hybrid Technique) 479
5. Comments and Discussions 480
6. Conclusion 481
Acknowledgments 481
References 481
Chapter 29: Effective finger vein-based authentication: Kernel principal component analysis 484
1. Introduction 484
2. Image Acquisition 485
3. Principal component analysis 486
4. Kernel principal component analysis 486
4.1. KPCA Algorithm 486
4.2. Kernel Feature Space versus PCA Feature Space 487
5. Experimental results 488
6. Conclusion 491
References 491
Chapter 30: Detecting distorted and benign blood cells using the Hough transform based on neural networks and decision trees 494
1. Introduction 494
2. Related work 496
3. Hough transforms 498
4. Overview of NN 499
5. Overview of the classification and regression tree 500
6. The proposed algorithm 500
7. The experimental results 503
8. Conclusions 508
References 509
Part 3: Registration, matching, and pattern recognition 512
Chapter 31: Improving performance with different length templates using both of correlation and absolute difference on si... 514
1. Introduction 514
2. Structure of the proposed method 515
3. 1D degeneration from videos 516
3.1. Motion Extraction from MPEG Videos and Construction of Space-Time Image 517
3.2. Motion Compensation Vectors from MPEG Videos 517
3.3. Space-Time Image 517
3.4. Matching Between Template ST Image and Retrieved ST Image 519
4. Similarity Measure with Correlation and Absolute Difference in Motion Retrieving Method 519
4.1. Similarity Measure in Motion Space-Time Image Based on Correlations 519
4.2. Similarity Measure in Motion Space-Time Image Based on Absolute Differences 520
5. Experiments on baseball games and evaluations 520
5.1. Baseball Game 520
5.2. Experimental Objects 521
5.3. Experiment Process 521
5.4. Correlation-Based Similarity Measure in Pitching Retrieval 521
5.5. Absolute Difference Based Similarity Measure in Pitching Retrieval 522
5.6. Combination Both of Correlations and Absolute Differences 522
6. Conclusions 524
References 524
Chapter 32: Surface registration by markers guided nonrigid iterative closest points algorithm 526
1. Introduction 526
2. Materials and methods 527
3. Results 529
4. Discussion and conclusions 529
Acknowledgments 532
References 534
Chapter 33: An affine shape constraint for geometric active contours 536
1. Introduction 536
2. Shape alignment using fourier descriptors 537
2.1. Euclidean Shapes Alignment 537
2.2. Affine Shape Alignment 539
2.2.1. Reparametrization of closed curve 539
2.2.2. Contours alignment using geometrical affine parameters estimation 540
Estimation of the scale factor a 540
Computation of the shift value l0 540
Computation of the matrix A's parameters 541
2.3. Discussion 541
2.4. Global Matching Using Affine Invariants Descriptors 542
3. Shape Prior for Geometric Active Contours 543
4. Experimental results 544
4.1. Robustness of the Proposed Shape Priors 544
4.2. Application to Object Detection 545
4.2.1. Case of Euclidean transformation 545
4.2.2. Case of affine transformation 547
5. Conclusions 550
References 551
Chapter 34: A topological approach for detection of chessboard patterns for camera calibration 554
1. Introduction 554
2. X-corner detector 556
3. Topological filter 557
4. Point Correspondences 559
5. Location refinement 560
6. Experimental Results 561
7. Conclusions 566
References 566
Chapter 35: Precision distortion correction technique based on FOV model for wide-angle cameras in automotive sector 570
1. Introduction 570
2. Related research 571
3. Distortion center estimation method using FOV model and 2D patterns 573
3.1. Distortion Correction Method Considering Distortion Center Estimation 573
3.2. FOV Distortion Model 574
3.3. Distortion Coefficient Estimation of the FOV Model 575
3.4. Distortion Center Estimation Method Using 2D Patterns 576
4. Experiment and evaluation 577
5. Application of algorithm to products improving vehicle convenience 582
5.1. Rear View Camera 583
5.2. Surround View Monitoring (SVM) System 583
6. Conclusion 584
Acknowledgments 585
References 585
Chapter 36: Distances and kernels based on cumulative distribution functions 588
1. Introduction 588
2. Distance and Similarity Measures Between Distributions 588
3. Distances on cumulative distribution functions 590
4. Experimental results and discussions 593
5. Generalization 595
6. Conclusions and Future Work 596
References 596
Chapter 37: Practical issues for binary code pattern unwrapping in fringe projection method 598
1. Introduction 598
2. Prior and related work 599
3. Practical issues for fringe pattern generation 599
4. Binary code generation for phase ambiguity resolution 603
5. Practical issues for projected fringe pattern photography 604
6. Three-dimensional reconstruction 606
6.1. How to Compute the Initial (Wrapped) Phase 606
6.2. How to Compute the Unwrapped Phase via Two Previous Outcomes 606
6.3. Noise Removal from Unwrapped Phase 609
6.4. Compute Differential Phase 609
6.5. Noise Removal from Differential Phase 610
6.6. How to Make RGB Texture Image from Projected Fringe Pattern Images 611
6.7. Object Cropping 612
6.8. Convert Differential Phase to Depth and 3D Visualization 613
6.9. Accuracy Evaluation of 3D Point Cloud 613
7. Summary and conclusions 616
References 617
Chapter 38: Detection and matching of object using proposed signature 620
1. Introduction 620
2. Overview on SURF method 621
3. Overview on Image Segmentation 623
4. The proposed algorithm 623
5. Experimental results 625
6. Conclusions 631
References 632
Index 634

Textauszug

Contributors

A. Abdel-Dayem     Department of Mathematics and Computer Science, Laurentian University, Sudbury, Ontario, Canada

Ryo Aita     Graduate school of Engineering, Utsunomiya University, 7-1-2, Yoto, Utsunomiya, Tochigi, Japan

Samet Akpınar     Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

Ferda Nur Alpaslan     Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

Kyota Aoki     Graduate school of Engineering, Utsunomiya University, 7-1-2, Yoto, Utsunomiya, Tochigi, Japan

Hamid R. Arabnia     University of Georgia, Computer Science, Athens, GA, USA

S. Arboleda-Duque

Department of Electric, Electronic and Computer Engineering, Universidad Nacional de Colombia

Department of Telecommunications Engineering, Universidad Católica de Manizales, Manizales, Caldas, Colombia

R. Ardekani     Molecular and Computational Biology, Department of Biological Sciences, USC, Los Angeles, CA, USA

Ramazan S. Aygün     DataMedia Research Lab, Computer Science Department, University of Alabama Huntsville, Huntsville, AL, USA

Pham The Bao     Faculty of Mathematics and Computer Science, Ho Chi Minh University of Science, Ho Chi Minh City, Viet Nam

Robert Beck     Department of Computing Sciences, Villanova University, Villanova, PA, USA

Christopher Blay     YouTube Corporation, San Bruno, CA, USA

H. Chen     Department of Preventive Medicine, Keck School of Medicine, USC, Los Angeles, CA, USA

Haijung Choi     SANE Co., Ltd, Seoul, Korea

Clarimar José Coelho     Computer Science and Computer Engineering Department (CMP), Pontifical Catholic University of Goiás (PUC-GO), Goiânia, Brazil

Eduardo Tavares Costa     Department of Biomedical Engineering, DEB/FEEC/UNICAMP, Campinas, Brazil

Anderson da Silva Soares     Computer Science Institute (INF), Federal University of Goiás (UFG), Goiânia, Brazil

Sepehr Damavandinejadmonfared     Department of Computing, Advanced Cyber Security Research Centre, Macquarie University, Sydney, New South Wales, Australia

Maria Stela Veludo de Paiva     Engineering School of São Carlos (EESC), Electrical Engineering Department, University of São Paulo (USP), São Paulo, Brazil

Leonidas Deligiannidis     Wentworth Institute of Technology, Department of Computer Science, Boston, MA, USA

İmren Dinç     DataMedia Research Lab, Computer Science Department, University of Alabama Huntsville, Huntsville, AL, USA

Semih Dinç     DataMedia Research Lab, Computer Science Department, University of Alabama Huntsville, Huntsville, AL, USA

Gregory Doerfler     Department of Computing Sciences, Villanova University, Villanova, PA, USA

Min Dong     School of Information Science and Engineering, Lanzhou University, Lanzhou, China

Arezoo Ektesabi     Swinburne University of Technology, Melbourne, Victoria, Australia

Hany A. Elsalamony     Mathematics Department, Faculty of Science, Helwan University, Cairo, Egypt

B. Foley     Molecular and Computational Biology, Department of Biological Sciences, USC, Los Angeles, CA, USA

Faouzi Ghorbel     GRIFT Research Group, CRISTAL Laboratory, Ecole Nationale des Sciences de l’Informatique (ENSI), Campus Universitaire de la Manouba, Manouba, Tunisia

J.B. Gómez-Mendoza     Department of Electric, Electronic and Computer Engineering, Universidad Nacional de Colombia, Manizales, Caldas, Colombia

Marco Aurélio Granero

Department of Biomedical Engineering, DEB/FEEC/UNICAMP, Campinas

Federal Institute of Education, Science and Technology São Paulo—IFSP, Sao Paulo, Brazil

Marco Antônio Gutierrez     Division of Informatics/Heart Institute, HCFMUSP, Sao Paulo, Brazil

M. Hariyama     Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan

A. Hematian     Department of Computer and Information Sciences, Towson University, Towson, MD, USA

Chuen-Min Huang     Department of Information Management, National Yunlin University of Science & Technology, Yunlin, Taiwan, ROC

Nguyen Tuan Hung     Faculty of Mathematics and Computer Science, Ho Chi Minh University of Science, Ho Chi Minh City, Viet Nam

M. Ilie     “Dunarea de Jos” University of Galati, Faculty of Automatic Control, Computers, Electrical and Electronics Engineering, Galati, Romania

Rowa’a Jamal     Electrical Engineering Department, University of Jordan, Amman, Jordan

J. Johnson     Department of Mathematics and Computer Science, Laurentian University, Sudbury, Ontario, Canada

Eui Sun Kang     Soongsil University, Seoul, Korea

Ajay Kapoor     Swinburne University of Technology, Melbourne, Victoria, Australia

A. Karimian     Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran

Loay Khalaf     Electrical Engineering Department, University of Jordan, Amman, Jordan

Jin Young Kim     School of Electrical and Computer Engineering, Chonnam National University, Gwangju, South Korea

Manbae Kim     Department of Computer and Communications Engineering, Kangwon National University, Chunchon, Gangwon, Republic of Korea

Bernd Klässner     Technische Universität München, München, Germany

Vladimir Kulyukin     Department of Computer Science, Utah State University, Logan, UT, USA

Gustavo Teodoro Laureano     Computer Science Institute (INF), Federal University of Goiás (UFG), Goiânia, Brazil

Xiangyu Lu     School of Information Science and Engineering, Lanzhou University, Lanzhou, China

Yide Ma     School of information Science Engineering, Lanzhou University, Lanzhou, China

Saeed Mahmoudpour     Department of Computer and Communications Engineering, Kangwon National University, Chunchon, Gangwon, Republic of Korea

Karmel Manaa     Electrical Engineering Department, University of Jordan, Amman, Jordan

P. Marjoram     Department of Preventive Medicine, Keck School of Medicine, USC, Los Angeles, CA, USA

Mohamed Amine Mezghich     GRIFT Research Group, CRISTAL Laboratory, Ecole Nationale des Sciences de l’Informatique (ENSI), Campus Universitaire de la Manouba, Manouba, Tunisia

Slim M’Hiri     GRIFT Research Group, CRISTAL Laboratory, Ecole Nationale des Sciences de l’Informatique (ENSI), Campus Universitaire de la Manouba, Manouba, Tunisia

José Manuel...

Produkt-Details

• Erscheint lt. Verlag: 9.12.2014
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Grafik / Design
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 0-12-802092-X / 012802092X
• ISBN-13: 978-0-12-802092-0 / 9780128020920