A comprehensive guide to the essential principles of image processing and pattern recognitionTechniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications.Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic.Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
Author(s): Frank Y. Shih
Edition: 1
Year: 2010
Language: English
Pages: 552
IMAGE PROCESSING AND PATTERN RECOGNITION......Page 4
CONTENTS......Page 8
PART I FUNDAMENTALS......Page 16
1 INTRODUCTION......Page 18
1.1.1 One-Dimensional Signals......Page 19
1.1.3 Three-Dimensional Signals......Page 20
1.2 Digital Image Processing......Page 21
1.3 Elements of an Image Processing System......Page 26
Appendix 1.A Selected List of Books on Image Processing and Computer Vision from Year 2000......Page 27
1.A.1 Selected List of Books on Signal Processing from Year 2000......Page 29
References......Page 30
2.1 Laplace Transform......Page 32
2.1.1 Properties of Laplace Transform......Page 34
2.2 Fourier Transform......Page 38
2.2.1 Basic Theorems......Page 39
2.2.2 Discrete Fourier Transform......Page 41
2.2.3 Fast Fourier Transform......Page 43
2.3 Z-Transform......Page 45
2.3.1 Definition of Z-Transform......Page 46
2.4 Cosine Transform......Page 47
2.5 Wavelet Transform......Page 49
References......Page 53
3 IMAGE ENHANCEMENT......Page 55
3.1 Grayscale Transformation......Page 56
3.2 Piecewise Linear Transformation......Page 57
3.4 Histogram Equalization......Page 60
3.5 Histogram Specification......Page 64
3.6 Enhancement by Arithmetic Operations......Page 66
3.7 Smoothing Filter......Page 67
3.8 Sharpening Filter......Page 70
3.9 Image Blur Types and Quality Measures......Page 74
References......Page 76
4 MATHEMATICAL MORPHOLOGY......Page 78
4.1.1 Binary Dilation......Page 79
4.1.2 Binary Erosion......Page 81
4.2 Opening and Closing......Page 83
4.3 Hit-or-Miss Transform......Page 84
4.4.1 Grayscale Dilation and Erosion......Page 86
4.4.2 Grayscale Dilation Erosion Duality Theorem......Page 90
4.5.1 Boundary Extraction......Page 91
4.5.3 Extraction of Connected Components......Page 92
4.5.4 Convex Hull......Page 93
4.5.5 Thinning......Page 95
4.5.6 Thickening......Page 96
4.5.7 Skeletonization......Page 97
4.5.8 Pruning......Page 99
4.5.9.1 The Simple Morphological Edge Operators......Page 100
4.5.9.2 Blur-Minimum Morphological Edge Operator......Page 102
4.6 Morphological Filters......Page 103
4.6.1 Alternating Sequential Filters......Page 104
4.6.2 Recursive Morphological Filters......Page 105
4.6.3 Soft Morphological Filters......Page 109
4.6.4 Order-Statistic Soft Morphological (OSSM) Filters......Page 114
4.6.5 Recursive Soft Morphological Filters......Page 117
4.6.6 Recursive Order-Statistic Soft Morphological Filters......Page 119
4.6.7 Regulated Morphological Filters......Page 121
4.6.8 Fuzzy Morphological Filters......Page 124
References......Page 129
5 IMAGE SEGMENTATION......Page 134
5.1 Thresholding......Page 135
5.2 Object (Component) Labeling......Page 137
5.3 Locating Object Contours by the Snake Model......Page 138
5.3.1 The Traditional Snake Model......Page 139
5.3.2 The Improved Snake Model......Page 140
5.3.3 The Gravitation External Force Field and The Greedy Algorithm......Page 143
5.3.4 Experimental Results......Page 144
5.4 Edge Operators......Page 145
5.5 Edge Linking by Adaptive Mathematical Morphology......Page 152
5.5.1 The Adaptive Mathematical Morphology......Page 153
5.5.2 The Adaptive Morphological Edge-Linking Algorithm......Page 155
5.5.3 Experimental Results......Page 156
5.6.1 Overview of the Automatic Seeded Region Growing Algorithm......Page 161
5.6.2 The Method for Automatic Seed Selection......Page 163
5.6.3 The Segmentation Algorithm......Page 165
5.6.4 Experimental Results and Discussions......Page 168
5.7 A Top-Down Region Dividing Approach......Page 173
5.7.2.1 Problem Motivation......Page 174
5.7.2.2 The TDRD-Based Image Segmentation......Page 176
5.7.3.1 Region Dividing Procedure......Page 177
5.7.3.2 Subregion Examination Strategy......Page 181
5.7.4 Experimental Results......Page 182
5.7.5.1 Breast Boundary Segmentation......Page 188
5.7.5.2 Lung Segmentation......Page 189
References......Page 190
6 DISTANCE TRANSFORMATION AND SHORTEST PATH PLANNING......Page 194
6.1 General Concept......Page 195
6.2 Distance Transformation by Mathematical Morphology......Page 199
6.3 Approximation of Euclidean Distance......Page 201
6.4 Decomposition of Distance Structuring Element......Page 203
6.4.1 Decomposition of City-Block and Chessboard Distance Structuring Elements......Page 204
6.4.2.1 Construction Procedure......Page 205
6.4.2.2 Computational Complexity......Page 207
6.5.2 Distance Functions in the 3D Domain......Page 208
6.6 The Acquiring Approaches......Page 209
6.6.1 Acquiring Approaches for City-Block and Chessboard Distance Transformations......Page 210
6.6.2 Acquiring Approach for Euclidean Distance Transformation......Page 211
6.7.1 The Fundamental Lemmas......Page 213
6.7.2 The Two-Scan Algorithm for EDT......Page 215
6.8 The Shortest Path Planning......Page 218
6.8.1 A Problematic Case of Using the Acquiring Approaches......Page 219
6.8.2 Dynamically Rotational Mathematical Morphology......Page 220
6.8.3 The Algorithm for Shortest Path Planning......Page 221
6.8.4 Some Examples......Page 222
6.9 Forward and Backward Chain Codes for Motion Planning......Page 224
6.10 A Few Examples......Page 228
References......Page 232
7.1 Run-Length Coding......Page 234
7.2 Binary Tree and Quadtree......Page 236
7.3 Contour Representation......Page 238
7.3.1 Chain Code and Crack Code......Page 239
7.3.2 Difference Chain Code......Page 241
7.3.4 The Mid-Crack Code......Page 242
7.4 Skeletonization by Thinning......Page 248
7.4.1 The Iterative Thinning Algorithm......Page 249
7.4.2 The Fully Parallel Thinning Algorithm......Page 250
7.4.2.1 Definition of Safe Point......Page 251
7.4.2.3 Deletability Conditions......Page 254
7.4.2.5 Experimental Results and Discussion......Page 258
7.5 Medial Axis Transformation......Page 259
7.5.1 Thick Skeleton Generation......Page 266
7.5.1.3 Trivial Uphill Generation......Page 268
7.5.2.3 Directional Uphill Generation......Page 269
7.5.2.4 Directional Downhill Generation......Page 270
7.5.3 The Skeletonization Algorithm and Connectivity Properties......Page 271
7.5.4 A Modified Algorithm......Page 274
7.6 Object Representation and Tolerance......Page 275
7.6.1 Representation Framework: Formal Languages and Mathematical Morphology......Page 276
7.6.2.1 The 2D Attributes......Page 277
7.6.2.3 Tolerancing Expression......Page 278
References......Page 280
8.1 Fourier Descriptor and Moment Invariants......Page 284
8.2.1 Shape Number......Page 289
8.2.2 Significant Points Radius and Coordinates......Page 291
8.2.3 Localization by Hierarchical Morphological Band-Pass Filter......Page 292
8.3 Corner Detection......Page 293
8.3.1 Asymmetrical Closing for Corner Detection......Page 295
8.3.2 Regulated Morphology for Corner Detection......Page 296
8.3.3 Experimental Results......Page 298
8.4 Hough Transform......Page 301
8.5 Principal Component Analysis......Page 304
8.6 Linear Discriminate Analysis......Page 306
8.7.1 Feature Reduction in the Input Space......Page 308
8.7.2 Feature Reduction in the Feature Space......Page 312
8.7.3 Combination of Input and Feature Spaces......Page 314
References......Page 317
9 PATTERN RECOGNITION......Page 321
9.1 The Unsupervised Clustering Algorithm......Page 322
9.1.1 Pass 1: Cluster’s Mean Vector Establishment......Page 323
9.1.2 Pass 2: Pixel Classification......Page 324
9.2 Bayes Classifier......Page 325
9.3.1 Linear Maximal Margin Classifier......Page 328
9.3.2 Linear Soft Margin Classifier......Page 330
9.3.3 Nonlinear Classifier......Page 331
9.3.4 SVM Networks......Page 332
9.4 Neural Networks......Page 335
9.4.1 Programmable Logic Neural Networks......Page 336
9.4.2 Pyramid Neural Network Structure......Page 338
9.4.3 Binary Morphological Operations by Logic Modules......Page 339
9.4.4 Multilayer Perceptron as Processing Modules......Page 342
9.5.1 The ART1 Model and Learning Process......Page 349
9.5.2.1 Learning in the ART2 Model......Page 352
9.5.2.2 Functional-Link Net Preprocessor......Page 354
9.5.3.1 Problem Analysis......Page 356
9.5.3.2 An Improved ART Model for Pattern Classification......Page 357
9.5.3.3 Experimental Results of the Improved Model......Page 359
9.6.1 Role of Fuzzy Geometry in Image Analysis......Page 361
9.6.2 Definitions of Fuzzy Sets......Page 362
9.6.3 Set Theoretic Operations......Page 363
References......Page 364
PART II APPLICATIONS......Page 368
10 FACE IMAGE PROCESSING AND ANALYSIS......Page 370
10.1 Face and Facial Feature Extraction......Page 371
10.1.1 Face Extraction......Page 372
10.1.2 Facial Feature Extraction......Page 377
10.1.3 Experimental Results......Page 382
10.2 Extraction of Head and Face Boundaries and Facial Features......Page 385
10.2.1.1 Smoothing and Thresholding......Page 387
10.2.1.4 Face Boundary Repairing......Page 389
10.2.2.1 Geometric Face Model......Page 390
10.2.2.2 Geometrical Face Model Based on Gabor Filter......Page 392
10.3 Recognizing Facial Action Units......Page 393
10.3.1 Facial Action Coding System and Expression Database......Page 394
10.3.2 The Proposed System......Page 397
10.3.3 Experimental Results......Page 398
10.4 Facial Expression Recognition in JAFFE Database......Page 401
10.4.1 The JAFFE Database......Page 403
10.4.2.2 Feature Extraction......Page 404
10.4.3 Experimental Results and Performance Comparisons......Page 405
References......Page 407
11 DOCUMENT IMAGE PROCESSING AND CLASSIFICATION......Page 412
11.1 Block Segmentation and Classification......Page 413
11.1.1 An Improved Two-Step Algorithm for Block Segmentation......Page 414
11.1.2 Rule-Based Block Classification......Page 415
11.1.3 Parameters Adaptation......Page 417
11.1.4 Experimental Results......Page 418
11.2 Rule-Based Character Recognition System......Page 422
11.3 Logo Identification......Page 426
11.4 Fuzzy Typographical Analysis for Character Preclassification......Page 429
11.4.1 Character Typographical Structure Analysis......Page 430
11.4.2 Baseline Detection......Page 431
11.4.3 Tolerance Analysis......Page 432
11.4.4 Fuzzy Typographical Categorization......Page 434
11.4.5 Experimental Results......Page 439
11.5 Fuzzy Model for Character Classification......Page 441
11.5.1 Similarity Measurement......Page 442
11.5.2 Statistical Fuzzy Model for Classification......Page 445
11.5.3 Similarity Measure in Fuzzy Model......Page 448
11.5.4 Matching Algorithm......Page 449
11.5.5 Classification Hierarchy......Page 451
11.5.6 Preclassifier for Grouping the Fuzzy Prototypes......Page 452
11.5.7 Experimental Results......Page 454
References......Page 456
12 IMAGE WATERMARKING......Page 459
12.1.1 Blind Versus Non Blind......Page 460
12.1.4 Robust Versus Fragile......Page 461
12.1.5 Spatial Domain Versus Frequency Domain......Page 462
12.2.1 Substitution Watermarking in the Spatial Domain......Page 463
12.2.2 Additive Watermarking in the Spatial Domain......Page 465
12.3.1 Substitution Watermarking in the Frequency Domain......Page 467
12.3.2 Multiplicative Watermarking in the Frequency Domain......Page 468
12.3.3 Watermarking Based on Vector Quantization......Page 470
12.3.4 Rounding Error Problem......Page 471
12.4.1 The Block-Based Fragile Watermark......Page 473
12.4.2 Weakness of the Block-Based Fragile Watermark......Page 474
12.4.3 The Hierarchical Block-Based Fragile Watermark......Page 475
12.5.1 The Redundant Embedding Approach......Page 476
12.6 Combinational Domain Digital Watermarking......Page 477
12.6.1 Overview of Combinational Watermarking......Page 478
12.6.2 Watermarking in the Spatial Domain......Page 479
12.6.3 The Watermarking in the Frequency Domain......Page 480
12.6.4 Experimental Results......Page 481
12.6.5 Further Encryption of Combinational Watermarking......Page 485
References......Page 486
13 IMAGE STEGANOGRAPHY......Page 489
13.1.1 Technical Steganography......Page 491
13.1.2 Linguistic Steganography......Page 492
13.2.1 Covert Communication......Page 493
13.2.2 One-Time Pad Communication......Page 494
13.4 Examples of Steganography Software......Page 495
13.4.4 JSteg-Jpeg......Page 496
13.5.1 Overview of the GA-Based Breaking Methodology......Page 497
13.5.2 The GA-Based Breaking Algorithm on SDSS......Page 500
13.5.2.2 Generating the Stego Image on the IQM-Based Steganalytic System (IQM-SDSS)......Page 501
13.5.3 The GA-Based Breaking Algorithm on FDSS......Page 502
13.5.4.1 The GA-Based Breaking Algorithm on VSS......Page 504
13.5.4.2 The GA-Based Breaking Algorithm on IQM-SDSS......Page 505
13.5.4.3 The GA-Based Breaking Algorithm on JFDSS......Page 506
13.5.5 Complexity Analysis......Page 508
References......Page 509
14.1 Automatic Extraction of Filaments......Page 511
14.1.1 Local Thresholding Based on Median Values......Page 512
14.1.2 Global Thresholding with Brightness and Area Normalization......Page 516
14.1.3 Feature Extraction......Page 521
14.1.4 Experimental Results......Page 526
14.2 Solar Flare Detection......Page 530
14.2.1 Feature Analysis and Preprocessing......Page 533
14.2.2 Classification Rates......Page 534
14.3 Solar Corona Mass Ejection Detection......Page 536
14.3.1 Preprocessing......Page 538
14.3.2.2 Features of CMEs......Page 540
14.3.3 Classification of Strong, Medium, and Weak CMEs......Page 541
14.3.4 Comparisons for CME Detections......Page 544
References......Page 546
INDEX......Page 550
