数字图像处理-(第三版)-英文版

本书特色

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本书是关于数字图像处理的经典著作，作者在对32个国家的134所院校和研究所的教师、学生及自学者进行广泛调查的基础上编写了第三版。除保留第二版的大部分主要内容外，还根据收集的建议从13个方面进行了修订，新增了400多幅图像、200多个图表和80多道习题，同时融入了近年来本科学领域的重要发展，使本书具有鲜明的特色与时效性。全书共分12章，包括绪论、数字图像基础、灰度变换与空间滤波、频域滤波、图像复原与重建、彩色图像处理、小波及多分辨率处理、图像压缩、形态学图像处理、图像分割、表现与描述、目标识别。

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内容简介

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本书是数字图像处理的经典教材，内容涵盖数字图像基础、灰度变换与空间滤波、频率域滤波、图像复原与重建、彩色图像处理、小波和多分辨率处理、图像压缩、形态学图像处理、图像分割、表示与描述、目标识别等，全球近700所高校采用为教材。

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作者简介

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　　　　Rafael C. Gonzalez（拉婓尔.冈萨雷斯）：美国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创始人、IEEE会士，研究领域为模式识别、图像处理和机器人，其著作已被全球范围内的600多所大学和研究所采用。
　　　　Richard E. Woods 美国田纳西大学电气工程系博士，IEEE会员。

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目录

Preface 15Acknowledgments 19The Book Web Site 20About the Authors 21Chapter 1 Introduction 231.1 What Is Digital Image Processing? 231.2 The Origins of Digital Image Processing 251.3 Examples of Fields that Use Digital Image Processing 291.3.1 Gamma-Ray Imaging 301.3.2 X-Ray Imaging 311.3.3 Imaging in the Ultraviolet Band 331.3.4 Imaging in the Visible and Infrared Bands 341.3.5 Imaging in the Microwave Band 401.3.6 Imaging in the Radio Band 421.3.7 Examples in which Other Imaging Modalities Are Used 421.4 Fundamental Steps in Digital Image Processing 471.5 Components of an Image Processing System 50Summary 53References and Further Reading 53Chapter 2 Digital Image Fundamentals 572.1 Elements of Visual Perception 582.1.1 Structure of the Human Eye 582.1.2 Image Formation in the Eye 602.1.3 Brightness Adaptation and Discrimination 612.2 Light and the Electromagnetic Spectrum 652.3 Image Sensing and Acquisition 682.3.1 Image Acquisition Using a Single Sensor 702.3.2 Image Acquisition Using Sensor Strips 702.3.3 Image Acquisition Using Sensor Arrays 722.3.4 A Simple Image Formation Model 722.4 Image Sampling and Quantization 742.4.1 Basic Concepts in Sampling and Quantization 742.4.2 Representing Digital Images 772.4.3 Spatial and Intensity Resolution 812.4.4 Image Interpolation 872.5 Some Basic Relationships between Pixels 902.5.1 Neighbors of a Pixel 902.5.2 Adjacency, Connectivity, Regions, and Boundaries 902.5.3 Distance Measures 932.6 An Introduction to the Mathematical Tools Used in Digital Image Processing 942.6.1 Array versus Matrix Operations 942.6.2 Linear versus Nonlinear Operations 952.6.3 Arithmetic Operations 962.6.4 Set and Logical Operations 1022.6.5 Spatial Operations 1072.6.6 Vector and Matrix Operations 1142.6.7 Image Transforms 1152.6.8 Probabilistic Methods 118Summary 120References and Further Reading 120Problems 121Chapter 3 Intensity Transformations and Spatial Filtering 1263.1 Background 1273.1.1 The Basics of Intensity Transformations and Spatial Filtering 1273.1.2 About the Examples in This Chapter 1293.2 Some Basic Intensity Transformation Functions 1293.2.1 Image Negatives 1303.2.2 Log Transformations 1313.2.3 Power-Law (Gamma) Transformations 1323.2.4 Piecewise-Linear Transformation Functions 1373.3 Histogram Processing 1423.3.1 Histogram Equalization 1443.3.2 Histogram Matching (Specification) 1503.3.3 Local Histogram Processing 1613.3.4 Using Histogram Statistics for Image Enhancement 1613.4 Fundamentals of Spatial Filtering 1663.4.1 The Mechanics of Spatial Filtering 1673.4.2 Spatial Correlation and Convolution 1683.4.3 Vector Representation of Linear Filtering 1723.4.4 Generating Spatial Filter Masks 1733.5 Smoothing Spatial Filters 1743.5.1 Smoothing Linear Filters 1743.5.2 Order-Statistic (Nonlinear) Filters 1783.6 Sharpening Spatial Filters 1793.6.1 Foundation 1803.6.2 Using the Second Derivative for Image Sharpening-The Laplacian 1823.6.3 Unsharp Masking and Highboost Filtering 1843.6.4 Using First-Order Derivatives for (Nonlinear) Image Sharpening—The Gradient 1873.7 Combining Spatial Enhancement Methods 1913.8 Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering 1953.8.1 Introduction 1953.8.2 Principles of Fuzzy Set Theory 1963.8.3 Using Fuzzy Sets 2003.8.4 Using Fuzzy Sets for Intensity Transformations 2083.8.5 Using Fuzzy Sets for Spatial Filtering 211Summary 214References and Further Reading 214Problems 215Chapter 4 Filtering in the Frequency Domain 2214.1 Background 2224.1.1 A Brief History of the Fourier Series and Transform 2224.1.2 About the Examples in this Chapter 2234.2 Preliminary Concepts 2244.2.1 Complex Numbers 2244.2.2 Fourier Series 2254.2.3 Impulses and Their Sifting Property 2254.2.4 The Fourier Transform of Functions of One Continuous Variable 2274.2.5 Convolution 2314.3 Sampling and the Fourier Transform of Sampled Functions 2334.3.1 Sampling 2334.3.2 The Fourier Transform of Sampled Functions 2344.3.3 The Sampling Theorem 2354.3.4 Aliasing 2394.3.5 Function Reconstruction (Recovery) from Sampled Data 2414.4 The Discrete Fourier Transform (DFT) of One Variable 2424.4.1 Obtaining the DFT from the Continuous Transform of a Sampled Function 2434.4.2 Relationship Between the Sampling and Frequency Intervals 2454.5 Extension to Functions of Two Variables 2474.5.1 The 2-D Impulse and Its Sifting Property 2474.5.2 The 2-D Continuous Fourier Transform Pair 2484.5.3 Two-Dimensional Sampling and the 2-D Sampling Theorem 2494.5.4 Aliasing in Images 2504.5.5 The 2-D Discrete Fourier Transform and Its Inverse 2574.6 Some Properties of the 2-D Discrete Fourier Transform 2584.6.1 Relationships Between Spatial and Frequency Intervals 2584.6.2 Translation and Rotation 2584.6.3 Periodicity 2594.6.4 Symmetry Properties 2614.6.5 Fourier Spectrum and Phase Angle 2674.6.6 The 2-D Convolution Theorem 2714.6.7 Summary of 2-D Discrete Fourier Transform Properties 2754.7 The Basics of Filtering in the Frequency Domain 2774.7.1 Additional Characteristics of the Frequency Domain 2774.7.2 Frequency Domain Filtering Fundamentals 2794.7.3 Summary of Steps for Filtering in the Frequency Domain 2854.7.4 Correspondence Between Filtering in the Spatial and Frequency Domains 2854.8 Image Smoothing Using Frequency Domain Filters 2914.8.1 Ideal Lowpass Filters 2914.8.2 Butterworth Lowpass Filters 2954.8.3 Gaussian Lowpass Filters 2984.8.4 Additional Examples of Lowpass Filtering 2994.9 Image Sharpening Using Frequency Domain Filters 3024.9.1 Ideal Highpass Filters 3034.9.2 Butterworth Highpass Filters 3064.9.3 Gaussian Highpass Filters 3074.9.4 The Laplacian in the Frequency Domain 3084.9.5 Unsharp Masking, Highboost Filtering, and High-Frequency-Emphasis Filtering 3104.9.6 Homomorphic Filtering 3114.10 Selective Filtering 3164.10.1 Bandreject and Bandpass Filters 3164.10.2 Notch Filters 3164.11 Implementation 3204.11.1 Separability of the 2-D DFT 3204.11.2 Computing the IDFT Using a DFT Algorithm 3214.11.3 The Fast Fourier Transform (FFT) 3214.11.4 Some Comments on Filter Design 325Summary 325References and Further Reading 326Problems 326Chapter 5 Image Restoration and Reconstruction 3335.1 A Model of the Image Degradation/Restoration Process 3345.2 Noise Models 3355.2.1 Spatial and Frequency Properties of Noise 3355.2.2 Some Important Noise Probability Density Functions 3365.2.3 Periodic Noise 3405.2.4 Estimation of Noise Parameters 3415.3 Restoration in the Presence of Noise Only—Spatial Filtering 3445.3.1 Mean Filters 3445.3.2 Order-Statistic Filters 3475.3.3 Adaptive Filters 3525.4 Periodic Noise Reduction by Frequency Domain Filtering 3575.4.1 Bandreject Filters 3575.4.2 Bandpass Filters 3585.4.3 Notch Filters 3595.4.4 Optimum Notch Filtering 3605.5 Linear, Position-Invariant Degradations 3655.6 Estimating the Degradation Function 3685.6.1 Estimation by Image Observation 3685.6.2 Estimation by Experimentation 3695.6.3 Estimation by Modeling 3695.7 Inverse Filtering 3735.8 Minimum Mean Square Error (Wiener) Filtering 3745.9 Constrained Least Squares Filtering 3795.10 Geometric Mean Filter 3835.11 Image Reconstruction from Projections 3845.11.1 Introduction 3845.11.2 Principles of Computed Tomography (CT) 3875.11.3 Projections and the Radon Transform 3905.11.4 The Fourier-Slice Theorem

封面

书名:数字图像处理-(第三版)-英文版

作者:拉斐尔.C.冈萨雷斯

页数:976

定价:¥89.0

出版社:电子工业出版社

出版日期:2017-01-01

ISBN:9787121305405

PDF电子书大小:54MB 高清扫描完整版