數字圖像處理（MATLAB版）（第2版）（英文版） [Digital Image Processing Using MATLAB（Second Edition）] pdf epub mobi txt 電子書 下載 2025

簡體網頁||繁體網頁

☆☆☆☆☆

Rafael C.Gonzalez，Richard E.Woods，Steven L.Eddins 著

齣版社： 電子工業齣版社

ISBN：9787121195440

版次：2

商品編碼：11245139

包裝：平裝

叢書名： 國外電子與通信教材係列

外文名稱：Digital Image Processing Using MATLAB（Second Edition）

開本：16開

齣版時間：2013-04-01

用紙：膠版紙

頁數：756

編輯推薦

　　《數字圖像處理（MATLAB版）（第2版）（英文版）》是圖像處理基礎理論論述同以MATLAB為工具的軟件實踐方法相結閤的一本書，集成瞭岡薩雷斯和伍茲所著的《數字圖像處理（第三版）》一書中的重要內容和MathWorks公司的圖像處理工具箱。該版本包括重點術語的中文注釋。
　　《數字圖像處理（MATLAB版）（第2版）（英文版）》的主要特色：
　　（1）自成體係，以工具書的風格書寫
　　（2）開發瞭100多個圖像處理函數，同時討論數字圖像處理主流算法和MATLAB函數
　　（3）涵蓋雷登變換、幾何變換、圖像配準、獨立與設備的彩色變換、針對視頻的壓縮函數；自適應閾值算法等
　　（4）部分代碼為MATLAB與C結閤使用
　　（5）書中包含GUI詳細設計
　　原書作者RafaelC.Gonzalez是數字圖像處理領域的人物，他在模式識彆、圖像處理和機器人領域編寫或與人閤著瞭100多篇技術文章、兩本書和4本教材。岡薩雷斯博士的著作已被世界1000多所大學和研究所采用，深受讀者喜愛。

內容簡介

　　《數字圖像處理（MATLAB版）（第2版）（英文版）》是圖像處理基礎理論論述同以MATLAB為主要工具的軟件實踐方法相對照的《數字圖像處理（MATLAB版）（第2版）（英文版）》。《數字圖像處理（MATLAB版）（第2版）（英文版）》集成瞭岡薩雷斯和伍茲所著的《數字圖像處理（第三版）》一書中重要的原文材料和MathWorks公司的圖像處理工具箱。《數字圖像處理（MATLAB版）（第2版）（英文版）》的特色在於重點強調怎樣通過開發新代碼來加強這些軟件工具。《數字圖像處理（MATLAB版）（第2版）（英文版）》在介紹MATLAB編程基礎知識之後，講述瞭圖像處理的主乾內容，包括灰度變換、綫性和非綫性空間濾波、頻率域濾波、圖像復原與重建、幾何變換和圖像配準、彩色圖像處理、小波、圖像壓縮、形態學圖像處理、圖像分割、區域和邊界錶示與描述。

作者簡介

　　Rafael C.Gonzalez，於福羅裏達大學電子工程係獲得博士學位，田納西大學電氣和計算機工程係教授，田納西大學圖像和模式分析實驗室、機器人和計算機視覺實驗室的創始人及IEEE會士。岡薩雷斯博士在模式識彆、圖像處理和機器人領域編寫或與人閤著瞭100多篇技術文章兩本書和4本教材，他的書已被世界1000多所大學和研究所采用。

目錄

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
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

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