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Practical image and video processing using MATLAB / Oge Marques.

By: Marques, Oge [author.].
Contributor(s): IEEE Xplore (Online Service) [distributor.] | John Wiley & Sons [publisher.].
Material type: materialTypeLabelBookPublisher: Hoboken, New Jersey : Wiley-IEEE Press, 2011Distributor: [Piscataqay, New Jersey] : IEEE Xplore, [2011]Description: 1 PDF (1,639 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781118093467.Subject(s): MATLAB | Image processing -- Mathematics | Image processing -- Digital techniques | Additives | Analog TV | Apertures | Arrays | Bars | Bibliographies | Books | Brightness | Cameras | Color | Computer languages | Computers | Convolution | Correlation | Cutoff frequency | DICOM | Data mining | Decoding | Degradation | Digital images | Electromagnetic spectrum | Equations | Feature extraction | Fourier transforms | Frequency domain analysis | Frequency response | Graphical user interfaces | Gray-scale | Histograms | History | Image coding | Image color analysis | Image edge detection | Image enhancement | Image processing | Image restoration | Image segmentation | Image sensors | Indexes | Interpolation | Lenses | Light sources | Lighting | MATLAB | Machine vision | Manganese | Mathematical model | Maximum likelihood detection | Memory management | Morphological operations | Morphology | Motion compensation | Motion estimation | Motion pictures | Motion segmentation | Noise | Noise measurement | Noise reduction | Nonlinear filters | Object segmentation | Observers | Optical imaging | Optical sensors | Photoreceptors | Polynomials | Probability | Programming | Redundancy | Retina | Sections | Sensors | Set theory | Shape | Spatial resolution | Standards | Streaming media | Support vector machines | Switches | Syntactics | Transforms | Tutorials | Video sequences | Visualization | WritingGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 502.85/66 Online resources: Abstract with links to resource Also available in print.
Contents:
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.
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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.

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