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数字图像处理概论 版权信息
- ISBN：9787562439677
- 条形码：9787562439677 ; 978-7-5624-3967-7
- 装帧：一般胶版纸
- 册数：暂无
- 重量：暂无
- 所属分类：>>>
数字图像处理概论 本书特色
数字图像处理是信息技术中的一门新兴学科，它所涵盖的内容极其丰富。麦克安德鲁编著的《数字图像处理概论》涵盖了低层次图像处理（图像增强、图像恢复）和高层次图像处理（形态学、图像分割、图像表示与描述和图像压缩）的全部基础内容，全书可作为图像处理专业课程本科生双语教学教材。
数字图像处理概论 内容简介
本书是美国Alasdair McAndrew所著Introduction to Digital Image Processing with MATLAB一书的缩编版。按照我国60学时的教学要求缩编而成。
缩编后的内容充实，涵盖了低层次图像处理（图像增强、图像恢复）和高层次图像处理（形态学、图像分割、图像表示与描述和图像压缩）的全部基础内容，习题和教材内容结合紧密，便于学生巩固所学知识。同时，缩编作者对原书部分难点进行了注释，帮助读者更好地理解所学内容。
本书可作为图像处理专业课程本科生双语教学教材。
数字图像处理概论 目录
PREFACE
CHAPTER 1 Introduction
1.1 hnages and Pictures
1.2 What is Image Processing?
1.3 Image Acquisition and Samplin~
1.4 Images and Digital Images
1.5 Some Applications
1.6 Aspects of Image Processing
1.7 An hnage-Processing Task
1.8 Types of Digital Images
1.9 Image File Sizes
1.10 Image Perception
Exercises
CHAPTER 2 Images and MATLAB
2.1 Grayscale Images
2.2 RGB Images
2.3 Indexed Color Images
2.4 Data Types and Conversions
2.5 Image Files and Formats
2.6 Files in MATLAB
Exercises
CHAPTER 3 Image Display
3.1 Introduction
3.2 Basics of Image Display
3.3 The imshow Function
3.4 Bit Planes
3.5 Spatial Resolution
3.6 Quantization mad Dithering
Exercises
CHAPTER 4 Point Processing
4.1 Introduction
4.2 Arithmetic Operations
4.3 Histograms
4.4 Lookup Tables
Exerciser
CHAPTER 5 Neighborhood Processing
5.1 Introduction
5.2 Notation
5.3 Filtering in MATLAB
5.4 Frequencies : Low-and High Pass Filters
5.5 Gaussian Filters
5.6 Edge Sharpening
5.7 Nonlinear Filters
Exercises
CHAPTER 6 Image Geometry
6.1 Interpolation of Data
6.2 hnage Interpolation
6.3 General Interpolation
6.4 Enlargement by Spatial Filtering
6.5 Scaling Smaller
6.6 Rotation
Exercises
CHAPTER 7 The Fourier Transform
7.1 Introduction
7.2 The Two-Dimensional DFF
7.3 Fourier Transforms in MATLAB
7.4 Fourier Transforms of Images
7.5 Filtering in the Frequency Domain
7.6 Homomorphic Filtering
Exercises
CHAPTER 8 Image Restoration
8.1 Introduction
8.2 Noise
8.3 Cleaning Salt and Pepper Noise
8.4 Cleaning Gaussian Noise
8.5 Removal of Periodic Noise
8.6 Inverse Filtering
8.7 Wiener Filtering
Exercises
CHAPTER 9 Image Segmentation
9.1 Introduction
9.2 Thresholding
9.3 Applications of Thresholding
9.4 Choosing an Appropriate Threshold Value
9.5 Adaptive Thresholding
9.6 Edge Detection
9.7 Derivatives and Edges
9.8 Second Derivatives
9.9 The Canny Edge Detector
……
CHAPTER 10 Mathematical Morphology
CHAPTER 11 Image Topology
CHAPTER 12 Shapes and Boundaries
CHAPTER 13 Color Procassing
CHAPTER 14 Image Coding and Compressdion
BIBLIOGRAPHY
TERMIOLOGIES USED IN THE BOOK
CHAPTER 1 Introduction
1.1 hnages and Pictures
1.2 What is Image Processing?
1.3 Image Acquisition and Samplin~
1.4 Images and Digital Images
1.5 Some Applications
1.6 Aspects of Image Processing
1.7 An hnage-Processing Task
1.8 Types of Digital Images
1.9 Image File Sizes
1.10 Image Perception
Exercises
CHAPTER 2 Images and MATLAB
2.1 Grayscale Images
2.2 RGB Images
2.3 Indexed Color Images
2.4 Data Types and Conversions
2.5 Image Files and Formats
2.6 Files in MATLAB
Exercises
CHAPTER 3 Image Display
3.1 Introduction
3.2 Basics of Image Display
3.3 The imshow Function
3.4 Bit Planes
3.5 Spatial Resolution
3.6 Quantization mad Dithering
Exercises
CHAPTER 4 Point Processing
4.1 Introduction
4.2 Arithmetic Operations
4.3 Histograms
4.4 Lookup Tables
Exerciser
CHAPTER 5 Neighborhood Processing
5.1 Introduction
5.2 Notation
5.3 Filtering in MATLAB
5.4 Frequencies : Low-and High Pass Filters
5.5 Gaussian Filters
5.6 Edge Sharpening
5.7 Nonlinear Filters
Exercises
CHAPTER 6 Image Geometry
6.1 Interpolation of Data
6.2 hnage Interpolation
6.3 General Interpolation
6.4 Enlargement by Spatial Filtering
6.5 Scaling Smaller
6.6 Rotation
Exercises
CHAPTER 7 The Fourier Transform
7.1 Introduction
7.2 The Two-Dimensional DFF
7.3 Fourier Transforms in MATLAB
7.4 Fourier Transforms of Images
7.5 Filtering in the Frequency Domain
7.6 Homomorphic Filtering
Exercises
CHAPTER 8 Image Restoration
8.1 Introduction
8.2 Noise
8.3 Cleaning Salt and Pepper Noise
8.4 Cleaning Gaussian Noise
8.5 Removal of Periodic Noise
8.6 Inverse Filtering
8.7 Wiener Filtering
Exercises
CHAPTER 9 Image Segmentation
9.1 Introduction
9.2 Thresholding
9.3 Applications of Thresholding
9.4 Choosing an Appropriate Threshold Value
9.5 Adaptive Thresholding
9.6 Edge Detection
9.7 Derivatives and Edges
9.8 Second Derivatives
9.9 The Canny Edge Detector
……
CHAPTER 10 Mathematical Morphology
CHAPTER 11 Image Topology
CHAPTER 12 Shapes and Boundaries
CHAPTER 13 Color Procassing
CHAPTER 14 Image Coding and Compressdion
BIBLIOGRAPHY
TERMIOLOGIES USED IN THE BOOK
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