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Practical Image and Video Processing Using MATLAB
ISBN-13: 9780470048153 / Angielski / Twarda / 2011 / 704 str.
Practical Image and Video Processing Using MATLAB
ISBN-13: 9780470048153 / Angielski / Twarda / 2011 / 704 str.
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Up-to-date, technically accurate coverage of essential topics in image and video processing This is the first book to combine image and video processing with a practical MATLAB(R)-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB(R) and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation. Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB(R). Extra features of this book include:
- More than 30 MATLAB(R) tutorials, which consist of step-by-step guides to exploring image and video processing techniques using MATLAB(R)
- Chapters supported by figures, examples, illustrative problems, and exercises
- Useful websites and an extensive list of bibliographical references
Supplemental resources for readers and instructors can be found at http: //www.ogemarques.com
LIST OF FIGURES xxi
LIST OF TABLES xxxix
FOREWORD xli
PREFACE xliii
ACKNOWLEDGMENTS xlix
PART I IMAGE PROCESSING
1 INTRODUCTION AND OVERVIEW 3
1.1 Motivation / 3
1.2 Basic Concepts and Terminology / 5
1.3 Examples of Typical Image Processing Operations / 6
1.4 Components of a Digital Image Processing System / 10
1.5 Machine Vision Systems / 12
1.6 Resources / 14
1.7 Problems / 18
2 IMAGE PROCESSING BASICS 21
2.1 Digital Image Representation / 21
2.1.1 Binary (1–Bit) Images / 23
2.1.2 Gray–Level (8–Bit) Images / 24
2.1.3 Color Images / 25
2.1.4 Compression / 26
2.2 Image File Formats / 27
2.3 Basic Terminology / 28
2.4 Overview of Image Processing Operations / 30
2.4.1 Global (Point) Operations / 31
2.4.2 Neighborhood–Oriented Operations / 31
2.4.3 Operations Combining Multiple Images / 32
2.4.4 Operations in a Transform Domain / 32
3 MATLAB BASICS 35
3.1 Introduction to MATLAB / 35
3.2 Basic Elements of MATLAB / 36
3.2.1 Working Environment / 36
3.2.2 Data Types / 37
3.2.3 Array and Matrix Indexing in MATLAB / 37
3.2.4 Standard Arrays / 37
3.2.5 Command–Line Operations / 38
3.3 Programming Tools: Scripts and Functions / 38
3.3.1 M–Files / 39
3.3.2 Operators / 40
3.3.3 Important Variables and Constants / 42
3.3.4 Number Representation / 42
3.3.5 Flow Control / 43
3.3.6 Code Optimization / 43
3.3.7 Input and Output / 43
3.4 Graphics and Visualization / 43
3.5 Tutorial 3.1: MATLAB a Guided Tour / 44
3.6 Tutorial 3.2: MATLAB Data Structures / 46
3.7 Tutorial 3.3: Programming in MATLAB / 53
3.8 Problems / 59
4 THE IMAGE PROCESSING TOOLBOX AT A GLANCE 61
4.1 The Image Processing Toolbox: an Overview / 61
4.2 Essential Functions and Features / 62
4.2.1 Displaying Information About an Image File / 62
4.2.2 Reading an Image File / 64
4.2.3 Data Classes and Data Conversions / 65
4.2.4 Displaying the Contents of an Image / 68
4.2.5 Exploring the Contents of an Image / 69
4.2.6 Writing the Resulting Image onto a File / 70
4.3 Tutorial 4.1: MATLAB Image Processing Toolbox a Guided Tour / 72
4.4 Tutorial 4.2: Basic Image Manipulation / 74
4.5 Problems / 80
5 IMAGE SENSING AND ACQUISITION 83
5.1 Introduction / 83
5.2 Light, Color, and Electromagnetic Spectrum / 84
5.2.1 Light and Electromagnetic Spectrum / 84
5.2.2 Types of Images / 85
5.2.3 Light and Color Perception / 86
5.2.4 Color Encoding and Representation / 87
5.3 Image Acquisition / 89
5.3.1 Image Sensors / 89
5.3.2 Camera Optics / 92
5.4 Image Digitization / 93
5.4.1 Sampling / 95
5.4.2 Quantization / 96
5.4.3 Spatial and Gray–Level Resolution / 97
5.5 Problems / 101
6 ARITHMETIC AND LOGIC OPERATIONS 103
6.1 Arithmetic Operations: Fundamentals and Applications / 103
6.1.1 Addition / 104
6.1.2 Subtraction / 106
6.1.3 Multiplication and Division / 109
6.1.4 Combining Several Arithmetic Operations / 110
6.2 Logic Operations: Fundamentals and Applications / 111
6.3 Tutorial 6.1: Arithmetic Operations / 113
6.4 Tutorial 6.2: Logic Operations and Region of Interest Processing / 118
6.5 Problems / 122
7 GEOMETRIC OPERATIONS 125
7.1 Introduction / 125
7.2 Mapping and Affine Transformations / 127
7.3 Interpolation Methods / 130
7.3.1 The Need for Interpolation / 130
7.3.2 A Simple Approach to Interpolation / 131
7.3.3 Zero–Order (Nearest–Neighbor) Interpolation / 132
7.3.4 First–Order (Bilinear) Interpolation / 132
7.3.5 Higher Order Interpolations / 132
7.4 Geometric Operations Using MATLAB / 132
7.4.1 Zooming, Shrinking, and Resizing / 133
7.4.2 Translation / 134
7.4.3 Rotation / 134
7.4.4 Cropping / 134
7.4.5 Flipping / 134
7.5 Other Geometric Operations and Applications / 134
7.5.1 Warping / 134
7.5.2 Nonlinear Image Transformations / 135
7.5.3 Morphing / 137
7.5.4 Seam Carving / 137
7.5.5 Image Registration / 137
7.6 Tutorial 7.1: Image Cropping, Resizing, Flipping, and Rotation / 138
7.7 Tutorial 7.2: Spatial Transformations and Image Registration / 142
7.8 Problems / 149
8 GRAY–LEVEL TRANSFORMATIONS 151
8.1 Introduction / 151
8.2 Overview of Gray–level (Point) Transformations / 152
8.3 Examples of Point Transformations / 155
8.3.1 Contrast Manipulation / 155
8.3.2 Negative / 157
8.3.3 Power Law (Gamma) Transformations / 157
8.3.4 Log Transformations / 159
8.3.5 Piecewise Linear Transformations / 160
8.4 Specifying the Transformation Function / 161
8.5 Tutorial 8.1: Gray–level Transformations / 163
8.6 Problems / 169
9 HISTOGRAM PROCESSING 171
9.1 Image Histogram: Definition and Example / 171
9.2 Computing Image Histograms / 173
9.3 Interpreting Image Histograms / 174
9.4 Histogram Equalization / 176
9.5 Direct Histogram Specification / 181
9.6 Other Histogram Modification Techniques / 184
9.6.1 Histogram Sliding / 185
9.6.2 Histogram Stretching / 185
9.6.3 Histogram Shrinking / 186
9.7 Tutorial 9.1: Image Histograms / 188
9.8 Tutorial 9.2: Histogram Equalization and Specification / 191
9.9 Tutorial 9.3: Other Histogram Modification Techniques / 195
9.10 Problems / 200
10 NEIGHBORHOOD PROCESSING 203
10.1 Neighborhood Processing / 203
10.2 Convolution and Correlation / 204
10.2.1 Convolution in the One–Dimensional Domain / 204
10.2.2 Convolution in the Two–Dimensional Domain / 206
10.2.3 Correlation / 208
10.2.4 Dealing with Image Borders / 210
10.3 Image Smoothing (Low–pass Filters) / 211
10.3.1 Mean Filter / 213
10.3.2 Variations / 213
10.3.3 Gaussian Blur Filter / 215
10.3.4 Median and Other Nonlinear Filters / 216
10.4 Image Sharpening (High–pass Filters) / 218
10.4.1 The Laplacian / 219
10.4.2 Composite Laplacian Mask / 220
10.4.3 Directional Difference Filters / 220
10.4.4 Unsharp Masking / 221
10.4.5 High–Boost Filtering / 221
10.5 Region of Interest Processing / 222
10.6 Combining Spatial Enhancement Methods / 223
10.7 Tutorial 10.1: Convolution and Correlation / 223
10.8 Tutorial 10.2: Smoothing Filters in the Spatial Domain / 225
10.9 Tutorial 10.3: Sharpening Filters in the Spatial Domain / 228
10.10 Problems / 234
11 FREQUENCY–DOMAIN FILTERING 235
11.1 Introduction / 235
11.2 Fourier Transform: the Mathematical Foundation / 237
11.2.1 Basic Concepts / 237
11.2.2 The 2D Discrete Fourier Transform: Mathematical Formulation / 239
11.2.3 Summary of Properties of the Fourier Transform / 241
11.2.4 Other Mathematical Transforms / 242
11.3 Low–pass Filtering / 243
11.3.1 Ideal LPF / 244
11.3.2 Gaussian LPF / 246
11.3.3 Butterworth LPF / 246
11.4 High–pass Filtering / 248
11.4.1 Ideal HPF / 248
11.4.2 Gaussian HPF / 250
11.4.3 Butterworth HPF / 250
11.4.4 High–Frequency Emphasis / 251
11.5 Tutorial 11.1: 2D Fourier Transform / 252
11.6 Tutorial 11.2: Low–pass Filters in the Frequency Domain / 254
11.7 Tutorial 11.3: High–pass Filters in the Frequency Domain / 258
11.8 Problems / 264
12 IMAGE RESTORATION 265
12.1 Modeling of the Image Degradation and Restoration Problem / 265
12.2 Noise and Noise Models / 266
12.2.1 Selected Noise Probability Density Functions / 267
12.2.2 Noise Estimation / 269
12.3 Noise Reduction Using Spatial–domain Techniques / 269
12.3.1 Mean Filters / 273
12.3.2 Order Statistic Filters / 275
12.3.3 Adaptive Filters / 278
12.4 Noise Reduction Using Frequency–domain Techniques / 278
12.4.1 Periodic Noise / 279
12.4.2 Bandreject Filter / 280
12.4.3 Bandpass Filter / 281
12.4.4 Notch Filter / 282
12.5 Image Deblurring Techniques / 283
12.5.1 Wiener Filtering / 286
12.6 Tutorial 12.1: Noise Reduction Using Spatial–domain Techniques / 289
12.7 Problems / 296
13 MORPHOLOGICAL IMAGE PROCESSING 299
13.1 Introduction / 299
13.2 Fundamental Concepts and Operations / 300
13.2.1 The Structuring Element / 301
13.3 Dilation and Erosion / 304
13.3.1 Dilation / 305
13.3.2 Erosion / 307
13.4 Compound Operations / 310
13.4.1 Opening / 310
13.4.2 Closing / 311
13.4.3 Hit–or–Miss Transform / 313
13.5 Morphological Filtering / 314
13.6 Basic Morphological Algorithms / 315
13.6.1 Boundary Extraction / 317
13.6.2 Region Filling / 319
13.6.3 Extraction and Labeling of Connected
Components / 321
13.7 Grayscale Morphology / 322
13.7.1 Dilation and Erosion / 323
13.7.2 Opening and Closing / 323
13.7.3 Top–Hat and Bottom–Hat Transformations / 325
13.8 Tutorial 13.1: Binary Morphological Image Processing / 325
13.9 Tutorial 13.2: Basic Morphological Algorithms / 330
13.10 Problems / 334
14 EDGE DETECTION 335
14.1 Formulation of the Problem / 335
14.2 Basic Concepts / 336
14.3 First–order Derivative Edge Detection / 338
14.4 Second–order Derivative Edge Detection / 343
14.4.1 Laplacian of Gaussian / 345
14.5 The Canny Edge Detector / 347
14.6 Edge Linking and Boundary Detection / 348
14.6.1 The Hough Transform / 349
14.7 Tutorial 14.1: Edge Detection / 354
14.8 Problems / 363
15 IMAGE SEGMENTATION 365
15.1 Introduction / 365
15.2 Intensity–based Segmentation / 367
15.2.1 Image Thresholding / 368
15.2.2 Global Thresholding / 369
15.2.3 The Impact of Illumination and Noise on Thresholding / 370
15.2.4 Local Thresholding / 371
15.3 Region–based Segmentation / 373
15.3.1 Region Growing / 374
15.3.2 Region Splitting and Merging / 377
15.4 Watershed Segmentation / 377
15.4.1 The Distance Transform / 378
15.5 Tutorial 15.1: Image Thresholding / 379
15.6 Problems / 386
16 COLOR IMAGE PROCESSING 387
16.1 The Psychophysics of Color / 387
16.1.1 Basic Concepts / 388
16.1.2 The CIE XYZ Chromaticity Diagram / 390
16.1.3 Perceptually Uniform Color Spaces / 393
16.1.4 ICC Profiles / 395
16.2 Color Models / 396
16.2.1 The RGB Color Model / 396
16.2.2 The CMY and CMYK Color Models / 398
16.2.3 The HSV Color Model / 398
16.2.4 The YIQ (NTSC) Color Model / 401
16.2.5 The YCbCr Color Model / 401
16.3 Representation of Color Images in MATLAB / 401
16.3.1 RGB Images / 402
16.3.2 Indexed Images / 403
16.4 Pseudocolor Image Processing / 406
16.4.1 Intensity Slicing / 406
16.4.2 Gray Level to Color Transformations / 407
16.4.3 Pseudocoloring in the Frequency Domain / 408
16.5 Full–color Image Processing / 409
16.5.1 Color Transformations / 410
16.5.2 Histogram Processing / 412
16.5.3 Color Image Smoothing and Sharpening / 412
16.5.4 Color Noise Reduction / 414
16.5.5 Color–Based Image Segmentation / 414
16.5.6 Color Edge Detection / 417
16.6 Tutorial 16.1: Pseudocolor Image Processing / 419
16.7 Tutorial 16.2: Full–color Image Processing / 420
16.8 Problems / 425
17 IMAGE COMPRESSION AND CODING 427
17.1 Introduction / 427
17.2 Basic Concepts / 428
17.2.1 Redundancy / 428
17.2.2 Image Encoding and Decoding Model / 431
17.3 Lossless and Lossy Compression Techniques / 432
17.3.1 Lossless Compression Techniques / 432
17.3.2 Lossy Compression Techniques / 433
17.4 Image Compression Standards / 435
17.4.1 Binary Image Compression Standards / 435
17.4.2 Continuous Tone Still Image Compression Standards / 435
17.4.3 JPEG / 436
17.4.4 JPEG 2000 / 437
17.4.5 JPEG–LS / 437
17.5 Image Quality Measures / 438
17.5.1 Subjective Quality Measurement / 438
17.5.2 Objective Quality Measurement / 439
17.6 Tutorial 17.1: Image Compression / 440
18 FEATURE EXTRACTION AND REPRESENTATION 447
18.1 Introduction / 447
18.2 Feature Vectors and Vector Spaces / 448
18.2.1 Invariance and Robustness / 449
18.3 Binary Object Features / 450
18.3.1 Area / 450
18.3.2 Centroid / 450
18.3.3 Axis of Least Second Moment / 451
18.3.4 Projections / 451
18.3.5 Euler Number / 452
18.3.6 Perimeter / 453
18.3.7 Thinness Ratio / 453
18.3.8 Eccentricity / 454
18.3.9 Aspect Ratio / 454
18.3.10 Moments / 455
18.4 Boundary Descriptors / 456
18.4.1 Chain Code, Freeman Code, and Shape Number / 459
18.4.2 Signatures / 461
18.4.3 Fourier Descriptors / 462
18.5 Histogram–based (Statistical) Features / 464
18.6 Texture Features / 466
18.7 Tutorial 18.1: Feature Extraction and Representation / 470
18.8 Problems / 474
19 VISUAL PATTERN RECOGNITION 475
19.1 Introduction / 475
19.2 Fundamentals / 476
19.2.1 Design and Implementation of a Visual Pattern Classifier / 476
19.2.2 Patterns and Pattern Classes / 478
19.2.3 Data Preprocessing / 479
19.2.4 Training and Test Sets / 480
19.2.5 Confusion Matrix / 480
19.2.6 System Errors / 481
19.2.7 Hit Rates, False Alarm Rates, and ROC Curves / 481
19.2.8 Precision and Recall / 482
19.2.9 Distance and Similarity Measures / 485
19.3 Statistical Pattern Classification Techniques / 487
19.3.1 Minimum Distance Classifier / 488
19.3.2 k–Nearest Neighbors Classifier / 490
19.3.3 Bayesian Classifier / 490
19.4 Tutorial 19.1: Pattern Classification / 491
19.5 Problems / 497
PART II VIDEO PROCESSING
20 VIDEO FUNDAMENTALS 501
20.1 Basic Concepts and Terminology / 501
20.2 Monochrome Analog Video / 507
20.2.1 Analog Video Raster / 507
20.2.2 Blanking Intervals / 508
20.2.3 Synchronization Signals / 509
20.2.4 Spectral Content of Composite Monochrome Analog Video / 509
20.3 Color in Video / 510
20.4 Analog Video Standards / 512
20.4.1 NTSC / 513
20.4.2 PAL / 513
20.4.3 SECAM / 514
20.4.4 HDTV / 514
20.5 Digital Video Basics / 514
20.5.1 Advantages of Digital Video / 515
20.5.2 Parameters of a Digital Video Sequence / 516
20.5.3 The Audio Component / 517
20.6 Analog–to–Digital Conversion / 517
20.7 Color Representation and Chroma Subsampling / 520
20.8 Digital Video Formats and Standards / 521
20.8.1 The Rec. 601 Digital Video Format / 522
20.8.2 The Common Intermediate Format / 523
20.8.3 The Source Intermediate Format / 524
20.9 Video Compression Techniques and Standards / 524
20.9.1 Video Compression Standards, Codecs, and Containers / 525
20.10 Video Processing in MATLAB / 526
20.10.1 Reading Video Files / 527
20.10.2 Processing Video Files / 527
20.10.3 Playing Video Files / 527
20.10.4 Writing Video Files / 528
20.11 Tutorial 20.1: Basic Digital Video Manipulation in MATLAB / 528
20.12 Tutorial 20.2: Working with YUV Video Data / 534
20.13 Problems / 539
21 VIDEO SAMPLING RATE AND STANDARDS CONVERSION 541
21.1 Video Sampling / 541
21.2 Sampling Rate Conversion / 542
21.3 Standards Conversion / 543
21.3.1 Deinterlacing / 543
21.3.2 Conversion between PAL and NTSC Signals / 545
21.3.3 Color Space Conversion / 545
21.3.4 Aspect Ratio Conversion / 546
21.3.5 3:2 Pull–Down / 547
21.4 Tutorial 21.1: Line Down–Conversion / 548
21.5 Tutorial 21.2: Deinterlacing / 550
21.6 Tutorial 21.3: NTSC to PAL Conversion / 556
21.7 Tutorial 21.4: 3:2 Pull–Down / 557
21.8 Problems / 559
22 DIGITAL VIDEO PROCESSING TECHNIQUES AND APPLICATIONS 561
22.1 Fundamentals of Motion Estimation and Motion Compensation / 561
22.2 General Methodologies in Motion Estimation / 564
22.2.1 Motion Representation / 566
22.2.2 Motion Estimation Criteria / 567
22.2.3 Optimization Methods / 567
22.3 Motion Estimation Algorithms / 568
22.3.1 Exhaustive Search Block Matching Algorithm / 568
22.3.2 Fast Algorithms / 570
22.3.3 Hierarchical Block Matching Algorithm / 571
22.3.4 Phase Correlation Method / 573
22.4 Video Enhancement and Noise Reduction / 573
22.4.1 Noise Reduction in Video / 574
22.4.2 Interframe Filtering Techniques / 575
22.5 Case Study: Object Segmentation and Tracking in the Presence of Complex Background / 576
22.6 Tutorial 22.1: Block–based Motion Estimation / 579
22.7 Tutorial 22.2: Intraframe and Interframe Filtering Techniques / 585
22.8 Problems / 589
Appendix A: HUMAN VISUAL PERCEPTION 591
A.1 Introduction / 591
A.2 The Human Eye / 592
A.3 Characteristics of Human Vision / 596
A.3.1 Resolution, Viewing Distance, and Viewing Angle / 596
A.3.2 Detail and Sharpness Perception / 598
A.3.3 Optical Transfer Function and Modulation Transfer Function / 599
A.3.4 Brightness Perception / 600
A.3.5 Contrast Ratio and Contrast Sensitivity Function / 603
A.3.6 Perception of Motion / 605
A.3.7 Spatiotemporal Resolution and Frequency Response / 606
A.3.8 Masking / 608
A.4 Implications and Applications of Knowledge about the Human Visual System / 609
Appendix B: GUI DEVELOPMENT 611
B.1 Introduction / 611
B.2 GUI File Structure / 611
B.3 Passing System Control / 613
B.4 The UserData Object / 615
B.5 A Working GUI Demo / 616
B.6 Concluding Remarks / 618
REFERENCES 619
INDEX 627
Oge Marques, PhD, is Associate Professor in the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University. He has been teaching and doing research on image and video processing for more than twenty years, in seven different countries. Dr. Marques is the coauthor of Processamento Digital de Imagens and Content–Based Image and Video Retrieval and was editor–in–chief of the Handbook of Video Databases, a comprehensive work with contributions from more than 100 world experts in the field. He is a Senior Member of both the IEEE and the ACM.
Up–to–date, technically accurate coverage of essential topics in image and video processing
This is the first book to combine image and video processing with a practical MATLAB®–oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation.
The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands–on activities and step–by–step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point–based, histogram–based, and neighborhood–based image enhancement techniques; the Fourier Transform and relevant frequency–domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation.
Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®.
Extra features of this book include:
-
More than 30 MATLAB® tutorials, which consist of step–by–step guides to exploring image and video processing techniques using MATLAB®
-
Chapters supported by figures, examples, illustrative problems, and exercises
-
Useful websites and an extensive list of bibliographical references
This accessible text is ideal for upper–level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.
Supplemental resources for readers and instructors can be found at http://www.ogemarques.com
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