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数字图像处理(MATLAB版)(第二版)(英文版)

  • 作者:Rafael C. Gonzalez
  • 出版社:电子工业出版社
  • ISBN:9787121195440
  • 出版日期:2013年04月01日
  • 页数:756
  • 定价:¥89.00
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    内容提要
    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**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**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。
    目录
    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 and Spatial
    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 Implementing Filters
    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—Spatial Filtering
    4.3.1 Spatial Noise Filters
    4.3.2 Adaptive Spatial Filters
    4.4 Periodic Noise Reduction Using Frequency Domain Filtering
    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 the Lucy-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 Filtered Backprojections
    4.11.4 Filter Implementation
    4.11.5 Reconstruction Using Fan-Beam Filtered Backprojections
    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-Perimeter Polygons
    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
    编辑推荐语
    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**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。

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