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）

　　本书是图像处理基础理论论述同以MATLAB为工具的软件实践方法相结合的**本书，集成了冈萨雷斯和伍兹所著的《数字图像处理（第三版）》一书中的重要内容和MathWorks公司的图像处理工具箱。该版本包括重点术语的中文注释。
　　本书的主要特色：
　　（1） 自成体系，以工具书的风格书写
　　（2） 开发了100多个图像处理函数，同时讨论数字图像处理主流算法和MATLAB函数
　　（3） 涵盖雷登变换、几何变换、图像配准、独立与设备的彩色变换、针对视频的压缩函数；自适应阈值算法等
　　（4） 部分代码为MATLAB与C结合使用
　　（5） 书中包含GUI详细设计
　　原书**作者Rafael C.Gonzalez是数字图像处理领域的权威人物，他在模式识别、图像处理和机器人领域编写或与人合著了100多篇技术文章、两本书和4本教材。冈萨雷斯博士的著作已被世界1000多所大学和研究所采用，深受读者喜爱。

　　这是图像处理基础理论论述同以MATLAB为主要工具的软件实践方法相对照的第一本书。本书集成了冈萨雷斯和伍兹所著的《数字图像处理（第三版）》一书中重要的原文材料和MathWorks公司的图像处理工具箱。本书的特色在于重点强调怎样通过开发新代码来加强这些软件工具。本书在介绍MATLAB编程基础知识之后，讲述了图像处理的主干内容，包括灰度变换、线性和非线性空间滤波、频率域滤波、图像复原与重建、几何变换和图像配准、彩色图像处理、小波、图像压缩、形态学图像处理、图像分割、区域和边界表示与描述。

Rafael C.Gonzalez于福罗里达大学电子工程系获得博士学位，田纳西大学电气和计算机工程系教授，田纳西大学图像和模式分析实验室、机器人和计算机视觉实验室的创始人及IEEE会士。冈萨雷斯博士在模式识别、图像处理和机器人领域编写或鱼人合著了100多篇技术文章两本书和4本教材，他的书已被世界1000多所大学和研究所采用。

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

Preface
This edition of Digital Image Processing Using MATLAB is a majorrevision of
the book. As in the previous edition, the focus of the book isbased on the fact that solutions to problems in the field ofdigital image processing generally require extensive experimentalwork involving software simulation and testing with large sets ofsample images. Although algorithm development typically is based ontheoretical underpinnings, the actual implementation of thesealgorithms almost always requires parameter estimation and,frequently, algorithm revision and comparison of candidatesolutions. Thus, selection of a flexible, comprehensive, andwell-documented software development environment is a key factorthat has important implications in the cost, development time, andportability of image processing solutions.
Despite its importance, surprisingly little has been written onthis aspect of the field in the form of textbook material dealingwith both theoretical principles and software implementation ofdigital image processing concepts. The first edition of this bookwas written in 2004 to meet just this need. This new edition of thebook continues the same focus. Its main objective is to provide afoundation for implementing image processing algorithms usingmodern software tools. A complementary objective is that the bookbe self-contained and easily readable by individuals with a basicbackground in digital image processing, mathematical analysis, andcomputer programming, all at a level typical of that found in ajunior/ senior curriculum in a technical discipline. Rudimentaryknowledge of MATLAB also is desirable.
To achieve these objectives, we felt that two key ingredients wereneeded. The
first was to select image processing material that isrepresentative of material covered in a formal course ofinstruction in this field. The second was to select software toolsthat are well supported and documented, and which have a wide rangeof applications in the “real” world.
To meet the first objective, most of the theoretical concepts inthe following
chapters were selected from Digital Image Processing by Gonzalezand Woods,
which has been the choice introductory textbook used by educatorsall over the
world for over three decades. The software tools selected are fromthe MATLAB＆reg;
Image Processing Toolbox, which similarly occupies a position ofeminence in
both education and industrial applications. A basic strategyfollowed in the preparation of the current edition was to continueproviding a seamless integration of well-established theoreticalconcepts and their implementation using state-of-theart softwaretools.
The book is organized along the same lines as Digital ImageProcessing. In
this way, the reader has easy access to a more detailed treatmentof all the image processing concepts discussed here, as well as anup-to-date set of references for further reading. Following thisapproach made it possible to present theoretical material in asuccinct manner and thus we were able to maintain a focus on thesoftware implementation aspects of image processing problemsolutions. Because it works in the MATLAB computing environment,the Image Processing Toolbox offers some significant advantages,not only in the breadth of its computationalalgorithm developmentand experimental work.
After an introduction to the fundamentals of MATLAB functions andprogramming,
the book proceeds to address the mainstream areas of imageprocessing.
The major areas covered include intensity transformations, fuzzyimage processing, linear and nonlinear spatial filtering, thefrequency domain filtering, image restoration and reconstruction,geometric transformations and image registration, color imageprocessing, wavelets, image data compression, morphological imageprocessing, image segmentation, region and boundary representationand description, and object recognition. This material iscomplemented by numerous illustrations of how to solve imageprocessing problems using MATLAB and toolbox functions.
In cases where a function did not exist, a new function was writtenand documented as part of the instructional focus of the book. Over120 new functions are included in the following chapters. Thesefunctions increase the scope of the Image Processing Toolbox byapproximately 40% and also serve the important purpose of furtherillustrating how to im