数字图像处理（MATLAB版）（第二版）（英文版）（冈萨雷斯数字图像处理经典教材的配套MATLAB实现） PDF下载

Contents
Preface
Acknowledgements
About the Authors
1 Introduction
　Preview
　1.1 Background
　1.2 What Is Digital Image Processing?
　1.3 Background on MATLAB and the Image Processing Toolbox
　1.4 Areas of Image Processing Covered in the Book
　1.5 The Book Web Site
　1.6 Notation
　1.7 Fundamentals
　1.7.1 The MATLAB Desktop
　1.7.2 Using the MATLAB Editor/Debugger
　1.7.3 Getting Help
　1.7.4 Saving and Retrieving Work Session Data
　1.7.5 Digital Image Representation
　1.7.6 Image I/O and Display
　1.7.7 Classes and Image Types
　1.7.8 M-Function Programming
　1.8 How References Are Organized in the Book
　Summary
2 Intensity Transformations and Spatial Filtering
　Preview
　2.1 Background
　2.2 Intensity Transformation Functions
　2.2.1 Functions imadjust and stretchlim
　2.2.2 Logarithmic and Contrast- Stretching Transformations
　2.2.3 Specifying Arbitrary Intensity Transformations
　2.2.4 Some Utility M-functions for Intensity Transformations
　2.3 Histogram Processing and Function Plotting
　2.3.1 Generating and Plotting Image Histograms
　2.3.2 Histogram Equalization
　2.3.3 Histogram Matching (Specification)
　2.3.4 Function adapthisteq
　2.4 Spatial Filtering
　2.4.1 Linear Spatial Filtering
　2.4.2 Nonlinear Spatial Filtering
　2.5 Image Processing Toolbox Standard Spatial Filters
　2.5.1 Linear Spatial Filters
　2.5.2 Nonlinear Spatial Filters
　2.6 Using Fuzzy Techniques for Intensity Transformations andSpatial
　Filtering
　2.6.1 Background
　2.6.2 Introduction to Fuzzy Sets
　2.6.3 Using Fuzzy Sets
　2.6.4 A Set of Custom Fuzzy M-functions
　2.6.5 Using Fuzzy Sets for Intensity Transformations
　2.6.6 Using Fuzzy Sets for Spatial Filtering
　Summary
3 Filtering in the Frequency Domain
　Preview
　3.1 The 2-D Discrete Fourier Transform
　3.2 Computing and Visualizing the 2-D DFT in MATLAB
　3.3 Filtering in the Frequency Domain
　3.3.1 Fundamentals
　3.3.2 Basic Steps in DFT Filtering
　3.3.3 An M-function for Filtering in the Frequency Domain
　3.4 Obtaining Frequency Domain Filters from Spatial Filters
　3.5 Generating Filters Directly in the Frequency Domain
　3.5.1 Creating Meshgrid Arrays for Use in ImplementingFilters
　in the Frequency Domain
　3.5.2 Lowpass (Smoothing) Frequency Domain Filters
　3.5.3 Wireframe and Surface Plotting
　3.6 Highpass (Sharpening) Frequency Domain Filters
　3.6.1 A Function for Highpass Filtering
　3.6.2 High-Frequency Emphasis Filtering
　3.7 Selective Filtering
　3.7.1 Bandreject and Bandpass Filters
　3.7.2 Notchreject and Notchpass Filters
　Summary
4 Image Restoration and Reconstruction
　Preview
　4.1 A Model of the Image Degradation/Restoration Process
　4.2 Noise Models
　4.2.1 Adding Noise to Images with Function imnoise
　4.2.2 Generating Spatial Random Noise with a Specified
　Distribution
　4.2.3 Periodic Noise
　4.2.4 Estimating Noise Parameters
　4.3 Restoration in the Presence of Noise Only—SpatialFiltering
　4.3.1 Spatial Noise Filters
　4.3.2 Adaptive Spatial Filters
　4.4 Periodic Noise Reduction Using Frequency DomainFiltering
　4.5 Modeling the Degradation Function
　4.6 Direct Inverse Filtering
　4.7 Wiener Filtering
　4.8 Constrained Least Squares (Regularized) Filtering
　4.9 Iterative Nonlinear Restoration Using theLucy-Richardson
　Algorithm
　4.10 Blind Deconvolution
　4.11 Image Reconstruction from Projections
　4.11.1 Background
　4.11.2 Parallel-Beam Projections and the Radon Transform
　4.11.3 The Fourier Slice Theorem and FilteredBackprojections
　4.11.4 Filter Implementation
　4.11.5 Reconstruction Using Fan-Beam FilteredBackprojections
　4.11.6 Function radon
　4.11.7 Function iradon
　4.11.8 Working with Fan-Beam Data
　Summary
5 Geometric Transformations and Image
　Registration
　Preview
　5.1 Transforming Points
　5.2 Affine Transformations
　5.3 Projective Transformations
　5.4 Applying Geometric Transformations to Images
　5.5 Image Coordinate Systems in MATLAB
　5.5.1 Output Image Location
　5.5.2 Controlling the Output Grid
　5.6 Image Interpolation
　5.6.1 Interpolation in Two Dimensions
　5.6.2 Comparing Interpolation Methods
　5.7 Image Registration
　5.7.1 Registration Process
　5.7.2 Manual Feature Selection and Matching Using cpselect
　5.7.3 Inferring Transformation Parameters Using cp2tform
　5.7.4 Visualizing Aligned Images
　5.7.5 Area-Based Registration
　5.7.6 Automatic Feature-Based Registration
　Summary
6 Color Image Processing
　Preview
　6.1 Color Image Representation in MATLAB
　6.1.1 RGB Images
　6.1.2 Indexed Images
　6.1.3 Functions for Manipulating RGB and Indexed Images
　6.2 Converting Between Color Spaces
　6.2.1 NTSC Color Space
　6.2.2 The YCbCr Color Space
　6.2.3 The HSV Color Space
　6.2.4 The CMY and CMYK Color Spaces
　6.2.5 The HSI Color Space
　6.2.6 Device-Independent Color Spaces
　6.3 The Basics of Color Image Processing
　6.4 Color Transformations
　6.5 Spatial Filtering of Color Images
　6.5.1 Color Image Smoothing
　6.5.2 Color Image Sharpening
　6.6 Working Directly in RGB Vector Space
　6.6.1 Color Edge Detection Using the Gradient
　6.6.2 Image Segmentation in RGB Vector Space
　Summary
7 Wavelets
　Preview
　7.1 Background
　7.2 The Fast Wavelet Transform
　7.2.1 FWTs Using the Wavelet Toolbox
　7.2.2 FWTs without the Wavelet Toolbox
　7.3 Working with Wavelet Decomposition Structures
　7.3.1 Editing Wavelet Decomposition Coefficients without the
　Wavelet Toolbox
　7.3.2 Displaying Wavelet Decomposition Coefficients
　7.4 The Inverse Fast Wavelet Transform
　7.5 Wavelets in Image Processing
　Summary
8 Image Compression
　Preview
　8.1 Background
　8.2 Coding Redundancy
　8.2.1 Huffman Codes
　8.2.2 Huffman Encoding
　8.2.3 Huffman Decoding
　8.3 Spatial Redundancy
　8.4 Irrelevant Information
　8.5 JPEG Compression
　8.5.1 JPEG
　8.5.2 JPEG 2000
　8.6 Video Compression
　8.6.1 MATLAB Image Sequences and Movies
　8.6.2 Temporal Redundancy and Motion Compensation
　Summary
9 Morphological Image Processing
　Preview
　9.1 Preliminaries
　9.1.1 Some Basic Concepts from Set Theory
　9.1.2 Binary Images, Sets, and Logical Operators
　9.2 Dilation and Erosion
　9.2.1 Dilation
　9.2.2 Structuring Element Decomposition
　9.2.3 The strel Function
　9.2.4 Erosion
　9.3 Combining Dilation and Erosion
　9.3.1 Opening and Closing
　9.3.2 The Hit-or-Miss Transformation
　9.3.3 Using Lookup Tables
　9.3.4 Function bwmorph
　9.4 Labeling Connected Components
　9.5 Morphological Reconstruction
　9.5.1 Opening by Reconstruction
　9.5.2 Filling Holes
　9.5.3 Clearing Border Objects
　9.6 Gray-Scale Morphology
　9.6.1 Dilation and Erosion
　9.6.2 Opening and Closing
　9.6.3 Reconstruction
　Summary
10 Image Segmentation
　Preview
　10.1 Point, Line, and Edge Detection
　10.1.1 Point Detection
　10.1.2 Line Detection
　10.1.3 Edge Detection Using Function edge
　10.2 Line Detection Using the Hough Transform
　10.2.1 Background
　10.2.2 Toolbox Hough Functions
　10.3 Thresholding
　10.3.1 Foundation
　10.3.2 Basic Global Thresholding
　10.3.3 Optimum Global Thresholding Using Otsu＇s Method
　10.3.4 Using Image Smoothing to Improve Global Thresholding
　10.3.5 Using Edges to Improve Global Thresholding
　10.3.6 Variable Thresholding Based on Local Statistics
　10.3.7 Image Thresholding Using Moving Averages
　10.4 Region-Based Segmentation
　10.4.1 Basic Formulation
　10.4.2 Region Growing
　10.4.3 Region Splitting and Merging
　10.5 Segmentation Using the Watershed Transform
　10.5.1 Watershed Segmentation Using the Distance Transform
　10.5.2 Watershed Segmentation Using Gradients
　10.5.3 Marker-Controlled Watershed Segmentation
　Summary
11 Representation and Description
　Preview
　11.1 Background
　11.1.1 Functions for Extracting Regions and Their Boundaries
　11.1.2 Some Additional MATLAB and Toolbox Functions Used
　in This Chapter
　11.1.3 Some Basic Utility M-Functions
　11.2 Representation
　11.2.1 Chain Codes
　11.2.2 Polygonal Approximations Using Minimum-PerimeterPolygons
　11.2.3 Signatures
　11.2.4 Boundary Segments
　11.2.5 Skeletons
　11.3 Boundary Descriptors
　11.3.1 Some Simple Descriptors
　11.3.2 Shape Numbers
　11.3.3 Fourier Descriptors
　11.3.4 Statistical Moments
　11.3.5 Corners
　11.4 Regional Descriptors
　11.4.1 Function regionprops
　11.4.2 Texture
　11.4.3 Moment Invariants
　11.5 Using Principal Components for Description
　Summary
　Appendix A M-Function Summary
　Appendix B ICE and MATLAB Graphical User Interfaces
　Appendix C Additional Custom M-functions
　Bibliography
　Index