Practical Image and Video Processing Using MATLAB

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...

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Bibliographic Details
Main Author: Marques, Oge
Format: eBook Book
Language:English
Published: Hoboken, NJ Wiley 2011
WILEY
John Wiley & Sons
John Wiley & Sons, Incorporated
Wiley-IEEE Press
Wiley-Blackwell
Edition:1
Series:Wiley - IEEE
Subjects:
Computing and Processing
Digital techniques
Digital video > Mathematics
Image processing
Image processing > Digital techniques
Image processing > Mathematics
Imaging Systems & Technology
Mathematics
MATLAB
TECHNOLOGY & ENGINEERING
TECHNOLOGY & ENGINEERING / Imaging Systems bisacsh
ISBN:0470048158, 9780470048153, 1118093488, 9781118093481, 111809347X, 9781118093474
Online Access:Get full text
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Table of 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-Base
  • Practical image and video processing using MATLAB® -- Dedication -- Contents -- List Of Figures -- List Of Tables -- Foreword -- Preface -- Acknowledgments -- PART I: Image Processing -- Chapter 1. Introduction And Overview -- Chapter 2. Image Processing Basics -- Chapter 3. MATLAB Basics -- Chapter 4. The Image Processing Toolbox At A Glance -- Chapter 5. Image Sensing And Acquisition -- Chapter 6. Arithmetic And Logic Operations -- Chapter 7. Geometric Operations -- Chapter 8. Gray-Level Transformations -- Chapter 9. Histogram Processing -- Chapter 10. Neighborhood Processing -- Chapter 11. Frequency-Domain Filtering -- Chapter 12. Image Restoration -- Chapter 13. Morphological Image Processing -- Chapter 14. Edge Detection -- Chapter 15. Image Segmentation -- Chapter 16. Color Image Processing -- Chapter 17. Image Compression And Coding -- Chapter 18. Feature Extraction And Representation -- Chapter 19. Visual Pattern Recognition -- PART II: Video Processing -- Chapter 20. Video Fundamentals -- Chapter 21. Video Sampling Rate And Standards Conversion -- Chapter 22. Digital Video Processing Techniques And Applications -- Appendix A: Human Visual Perception -- Appendix B: GUI Development -- References -- Index
  • 8 GRAY-LEVEL TRANSFORMATIONS -- 8.1 Introduction -- 8.2 Overview of Gray-level (Point) Transformations -- 8.3 Examples of Point Transformations -- 8.3.1 Contrast Manipulation -- 8.3.2 Negative -- 8.3.3 Power Law (Gamma) Transformations -- 8.3.4 Log Transformations -- 8.3.5 Piecewise Linear Transformations -- 8.4 Specifying the Transformation Function -- 8.5 Tutorial 8.1: Gray-level Transformations -- 8.6 Problems -- 9 HISTOGRAM PROCESSING -- 9.1 Image Histogram: Definition and Example -- 9.2 Computing Image Histograms -- 9.3 Interpreting Image Histograms -- 9.4 Histogram Equalization -- 9.5 Direct Histogram Specification -- 9.6 Other Histogram Modification Techniques -- 9.6.1 Histogram Sliding -- 9.6.2 Histogram Stretching -- 9.6.3 Histogram Shrinking -- 9.7 Tutorial 9.1: Image Histograms -- 9.8 Tutorial 9.2: Histogram Equalization and Specification -- 9.9 Tutorial 9.3: Other Histogram Modification Techniques -- 9.10 Problems -- 10 NEIGHBORHOOD PROCESSING -- 10.1 Neighborhood Processing -- 10.2 Convolution and Correlation -- 10.2.1 Convolution in the One-Dimensional Domain -- 10.2.2 Convolution in the Two-Dimensional Domain -- 10.2.3 Correlation -- 10.2.4 Dealing with Image Borders -- 10.3 Image Smoothing (Low-pass Filters) -- 10.3.1 Mean Filter -- 10.3.2 Variations -- 10.3.3 Gaussian Blur Filter -- 10.3.4 Median and Other Nonlinear Filters -- 10.4 Image Sharpening (High-pass Filters) -- 10.4.1 The Laplacian -- 10.4.2 Composite Laplacian Mask -- 10.4.3 Directional Difference Filters -- 10.4.4 Unsharp Masking -- 10.4.5 High-Boost Filtering -- 10.5 Region of Interest Processing -- 10.6 Combining Spatial Enhancement Methods -- 10.7 Tutorial 10.1: Convolution and Correlation -- 10.8 Tutorial 10.2: Smoothing Filters in the Spatial Domain -- 10.9 Tutorial 10.3: Sharpening Filters in the Spatial Domain -- 10.10 Problems -- 11 FREQUENCY-DOMAIN FILTERING
  • 13.7.2 Opening and Closing -- 13.7.3 Top-Hat and Bottom-Hat Transformations -- 13.8 Tutorial 13.1: Binary Morphological Image Processing -- 13.9 Tutorial 13.2: Basic Morphological Algorithms -- 13.10 Problems -- 14 EDGE DETECTION -- 14.1 Formulation of the Problem -- 14.2 Basic Concepts -- 14.3 First-order Derivative Edge Detection -- 14.4 Second-order Derivative Edge Detection -- 14.4.1 Laplacian of Gaussian -- 14.5 The Canny Edge Detector -- 14.6 Edge Linking and Boundary Detection -- 14.6.1 The Hough Transform -- 14.7 Tutorial 14.1: Edge Detection -- 14.8 Problems -- 15 IMAGE SEGMENTATION -- 15.1 Introduction -- 15.2 Intensity-based Segmentation -- 15.2.1 Image Thresholding -- 15.2.2 Global Thresholding -- 15.2.3 The Impact of Illumination and Noise on Thresholding -- 15.2.4 Local Thresholding -- 15.3 Region-based Segmentation -- 15.3.1 Region Growing -- 15.3.2 Region Splitting and Merging -- 15.4 Watershed Segmentation -- 15.4.1 The Distance Transform -- 15.5 Tutorial 15.1: Image Thresholding -- 15.6 Problems -- 16 COLOR IMAGE PROCESSING -- 16.1 The Psychophysics of Color -- 16.1.1 Basic Concepts -- 16.1.2 The CIE XYZ Chromaticity Diagram -- 16.1.3 Perceptually Uniform Color Spaces -- 16.1.4 ICC Profiles -- 16.2 Color Models -- 16.2.1 The RGB Color Model -- 16.2.2 The CMY and CMYK Color Models -- 16.2.3 The HSV Color Model -- 16.2.4 The YIQ (NTSC) Color Model -- 16.2.5 The YCbCr Color Model -- 16.3 Representation of Color Images in MATLAB -- 16.3.1 RGB Images -- 16.3.2 Indexed Images -- 16.4 Pseudocolor Image Processing -- 16.4.1 Intensity Slicing -- 16.4.2 Gray Level to Color Transformations -- 16.4.3 Pseudocoloring in the Frequency Domain -- 16.5 Full-color Image Processing -- 16.5.1 Color Transformations -- 16.5.2 Histogram Processing -- 16.5.3 Color Image Smoothing and Sharpening -- 16.5.4 Color Noise Reduction
  • Practical Image and Video Processing Using MATLAB® -- CONTENTS -- LIST OF FIGURES -- LIST OF TABLES -- FOREWORD -- PREFACE -- ACKNOWLEDGMENTS -- PART I: IMAGE PROCESSING -- 1 INTRODUCTION AND OVERVIEW -- 1.1 Motivation -- 1.2 Basic Concepts and Terminology -- 1.3 Examples of Typical Image Processing Operations -- 1.4 Components of a Digital Image Processing System -- 1.5 Machine Vision Systems -- 1.6 Resources -- 1.7 Problems -- 2 IMAGE PROCESSING BASICS -- 2.1 Digital Image Representation -- 2.1.1 Binary (1-Bit) Images -- 2.1.2 Gray-Level (8-Bit) Images -- 2.1.3 Color Images -- 2.1.4 Compression -- 2.2 Image File Formats -- 2.3 Basic Terminology -- 2.4 Overview of Image Processing Operations -- 2.4.1 Global (Point) Operations -- 2.4.2 Neighborhood-Oriented Operations -- 2.4.3 Operations Combining Multiple Images -- 2.4.4 Operations in a Transform Domain -- 3 MATLAB BASICS -- 3.1 Introduction to MATLAB -- 3.2 Basic Elements of MATLAB -- 3.2.1 Working Environment -- 3.2.2 Data Types -- 3.2.3 Array and Matrix Indexing in MATLAB -- 3.2.4 Standard Arrays -- 3.2.5 Command-Line Operations -- 3.3 Programming Tools: Scripts and Functions -- 3.3.1 M-Files -- 3.3.2 Operators -- 3.3.3 Important Variables and Constants -- 3.3.4 Number Representation -- 3.3.5 Flow Control -- 3.3.6 Code Optimization -- 3.3.7 Input and Output -- 3.4 Graphics and Visualization -- 3.5 Tutorial 3.1: MATLAB-a Guided Tour -- 3.6 Tutorial 3.2: MATLAB Data Structures -- 3.7 Tutorial 3.3: Programming in MATLAB -- 3.8 Problems -- 4 THE IMAGE PROCESSING TOOLBOX AT A GLANCE -- 4.1 The Image Processing Toolbox: an Overview -- 4.2 Essential Functions and Features -- 4.2.1 Displaying Information About an Image File -- 4.2.2 Reading an Image File -- 4.2.3 Data Classes and Data Conversions -- 4.2.4 Displaying the Contents of an Image -- 4.2.5 Exploring the Contents of an Image
  • 16.5.5 Color-Based Image Segmentation
  • 4.2.6 Writing the Resulting Image onto a File -- 4.3 Tutorial 4.1: MATLAB Image Processing Toolbox-a Guided Tour -- 4.4 Tutorial 4.2: Basic Image Manipulation -- 4.5 Problems -- 5 IMAGE SENSING AND ACQUISITION -- 5.1 Introduction -- 5.2 Light, Color, and Electromagnetic Spectrum -- 5.2.1 Light and Electromagnetic Spectrum -- 5.2.2 Types of Images -- 5.2.3 Light and Color Perception -- 5.2.4 Color Encoding and Representation -- 5.3 Image Acquisition -- 5.3.1 Image Sensors -- 5.3.2 Camera Optics -- 5.4 Image Digitization -- 5.4.1 Sampling -- 5.4.2 Quantization -- 5.4.3 Spatial and Gray-Level Resolution -- 5.5 Problems -- 6 ARITHMETIC AND LOGIC OPERATIONS -- 6.1 Arithmetic Operations: Fundamentals and Applications -- 6.1.1 Addition -- 6.1.2 Subtraction -- 6.1.3 Multiplication and Division -- 6.1.4 Combining Several Arithmetic Operations -- 6.2 Logic Operations: Fundamentals and Applications -- 6.3 Tutorial 6.1: Arithmetic Operations -- 6.4 Tutorial 6.2: Logic Operations and Region of Interest Processing -- 6.5 Problems -- 7 GEOMETRIC OPERATIONS -- 7.1 Introduction -- 7.2 Mapping and Affine Transformations -- 7.3 Interpolation Methods -- 7.3.1 The Need for Interpolation -- 7.3.2 A Simple Approach to Interpolation -- 7.3.3 Zero-Order (Nearest-Neighbor) Interpolation -- 7.3.4 First-Order (Bilinear) Interpolation -- 7.3.5 Higher Order Interpolations -- 7.4 Geometric Operations Using MATLAB -- 7.4.1 Zooming, Shrinking, and Resizing -- 7.4.2 Translation -- 7.4.3 Rotation -- 7.4.4 Cropping -- 7.4.5 Flipping -- 7.5 Other Geometric Operations and Applications -- 7.5.1 Warping -- 7.5.2 Nonlinear Image Transformations -- 7.5.3 Morphing -- 7.5.4 Seam Carving -- 7.5.5 Image Registration -- 7.6 Tutorial 7.1: Image Cropping, Resizing, Flipping, and Rotation -- 7.7 Tutorial 7.2: Spatial Transformations and Image Registration -- 7.8 Problems
  • 11.1 Introduction -- 11.2 Fourier Transform: the Mathematical Foundation -- 11.2.1 Basic Concepts -- 11.2.2 The 2D Discrete Fourier Transform: Mathematical Formulation -- 11.2.3 Summary of Properties of the Fourier Transform -- 11.2.4 Other Mathematical Transforms -- 11.3 Low-pass Filtering -- 11.3.1 Ideal LPF -- 11.3.2 Gaussian LPF -- 11.3.3 Butterworth LPF -- 11.4 High-pass Filtering -- 11.4.1 Ideal HPF -- 11.4.2 Gaussian HPF -- 11.4.3 Butterworth HPF -- 11.4.4 High-Frequency Emphasis -- 11.5 Tutorial 11.1: 2D Fourier Transform -- 11.6 Tutorial 11.2: Low-pass Filters in the Frequency Domain -- 11.7 Tutorial 11.3: High-pass Filters in the Frequency Domain -- 11.8 Problems -- 12 IMAGE RESTORATION -- 12.1 Modeling of the Image Degradation and Restoration Problem -- 12.2 Noise and Noise Models -- 12.2.1 Selected Noise Probability Density Functions -- 12.2.2 Noise Estimation -- 12.3 Noise Reduction Using Spatial-domain Techniques -- 12.3.1 Mean Filters -- 12.3.2 Order Statistic Filters -- 12.3.3 Adaptive Filters -- 12.4 Noise Reduction Using Frequency-domain Techniques -- 12.4.1 Periodic Noise -- 12.4.2 Bandreject Filter -- 12.4.3 Bandpass Filter -- 12.4.4 Notch Filter -- 12.5 Image Deblurring Techniques -- 12.5.1 Wiener Filtering -- 12.6 Tutorial 12.1: Noise Reduction Using Spatial-domain Techniques -- 12.7 Problems -- 13 MORPHOLOGICAL IMAGE PROCESSING -- 13.1 Introduction -- 13.2 Fundamental Concepts and Operations -- 13.2.1 The Structuring Element -- 13.3 DILATION AND EROSION -- 13.3.1 Dilation -- 13.3.2 Erosion -- 13.4 Compound Operations -- 13.4.1 Opening -- 13.4.2 Closing -- 13.4.3 Hit-or-Miss Transform -- 13.5 Morphological Filtering -- 13.6 Basic Morphological Algorithms -- 13.6.1 Boundary Extraction -- 13.6.2 Region Filling -- 13.6.3 Extraction and Labeling of Connected Components -- 13.7 Grayscale Morphology -- 13.7.1 Dilation and Erosion