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.

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.

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.

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.

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.

16.5.5 Color-Based Image Segmentation.