数字图像处理 : 第3版
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作者(美)冈萨雷斯,(美)伍兹 著
出版社电子工业出版社
ISBN9787121102073
出版时间2010-01
版次1
装帧平装
开本16开
纸张胶版纸
页数976页
字数99999千字
定价79.8元
上书时间2024-04-05
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书名:数字图像处理 : 第3版
定价：79.8元
作者:(美)冈萨雷斯,(美)伍兹 著
出版社：电子工业出版社
出版日期：2010-01-01
ISBN：9787121102073
字数：1776000
页码：976
版次：1
装帧：平装
开本：16开
商品重量：
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内容提要
目录
PrefaceAcknowledgmentsThe Book Web SiteAbout the Authors1 Introduction 1.1 What Is Digital Image Processing? 1.2 The Origins of Digital Image Processing 1.3 Examples of Fields that Use Digital Image Processing 1.4 Fundamental Steps in Digital Image Processing 1.5 Components of an Image Processing System Summary References and Further Reading2 Digital Image Fundamentals 2.1 Elements of Visual Perception 2.2 Light and the Electromagnetic Spectrum 2.3 Image Sensing and Acquisition 2.4 Image Sampling and Quantization 2.5 Some Basic Relationshipetween Pixels 2.6 An Introduction to the Mathematical Tools Used in Digital Image Processing Summary 2.5 Some Basic Relationshipetween Pixels 2.5.1 Neiors of a Pixel 2.5.2 Adjacency, Connectivity, Regions, and Boundaries 2.5.3 Distance Measures 2.6 An Introduction to the Mathematical Tools Used in Digital Image Processing 2.6.1 Array versus Matrix Operations 2.6.2 Linear versus Nonlinear Operations 2.6.3 Arithmetic Operations 2.6.4 Set and Logical Operations 2.6.5 Spatial Operations 2.6.6 Vector and Matrix Operations 2.6.7 Image Transforms 2.6.8 Probabilistic Methods Summary References and Further Reading Problems 3 Intensity Transformations and Spatial Filtering 3.1 Background 3.1.1 The Basics of Intensity Transformations and Spatial Filtering 3.1.2 About the Examples in This Chapter 3.2 Some Basic Intensity Transformation Functions 3.2.1 Image s 3.2.2 Log Transformations 3.2.3 Power-Law (Gamma) Transformations 3.2.4 Piecewise-Linear Transformation Functions 3.3 Histogram Processing 3.3.1 Histogram Equalization 3.3.2 Histogram Matching (Specification) 3.3.3 Local Histogram Processing 3.3.4 Using Histogram Statistics for Image Enhancement 3.4 Fundamentals of Spatial Filtering 3.4.1 The Mechanics of Spatial Filtering 3.4.2 Spatial Correlation and Convolution 3.4.3 Vector Representation of Linear Filtering 3.4.4 Generating Spatial Filter Masks 3.5 Smoothing Spatial Filters 3.5.1 Smoothing Linear Filters 3.5.2 Order-Statistic (Nonlinear) Filters 3.6 Sharpening Spatial Filters 3.6.1 Foundation 3.6.2 Using the Second Derivative for Image Sharpening--The Laplacian ……
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