计算机视觉特征提取与图像处理（第三版）（英文版） / 国外电子与通信教材系列
￥89.00定价
作者： 马克 S. 尼克松，阿尔贝托 S. 阿瓜多
出版时间：2013-02
出版社：电子工业出版社
- 电子工业出版社
- 9787121195273
- 1-1
- 146094
- 47151607-0
- 平装
- 16开
- 2013-02
- 1143
- 628
- 工学
- 计算机科学与技术
- TP391.41
- 信号与信息处理
- 研究生（硕士、EMBA、MBA、MPA、博士）
内容简介
尼克松和阿瓜多编著的《计算机视觉特征提取与图像处理》是由英国南安普顿大学的MarkNixon教授和Sportradar公司的Alberto uado在第二版的基础上，于2012年9月推出的最新改版之作。本次改版，主要的变化是将高级特征提取，分为固定形状匹配与可变形形状分析两部分，并增加了新的一章内容：运动对象检测与描述。具体地，在简要介绍计算机视觉的基础概念和基本的图像处理运算后，重点讨论了低级和高级的特征提取，包括边缘检测、固定形状匹配和可变形形状分析。此外，对目标描述，纹理描述、分割及分类，以及运动对象检测等都进行深入的阐述。它突出了计算机视觉的主要问题——特征提取，以清晰、简洁的语言，阐述了图像处理和计算机视觉的基础理论与技术。
《计算机视觉特征提取与图像处理》可作为高等学校电子工程、计算机科学、计算机工程等专业本科生的双语教材，也可以作为图像、视频信号处理，模式识别和计算机视觉研究方向的博士生、硕士研究生，以及相关专业的科研工作者的参考用书。
《计算机视觉特征提取与图像处理》可作为高等学校电子工程、计算机科学、计算机工程等专业本科生的双语教材，也可以作为图像、视频信号处理，模式识别和计算机视觉研究方向的博士生、硕士研究生，以及相关专业的科研工作者的参考用书。
目录
Preface
About the authors
CHAPTER 1 Introduction
1.1 Overview
1.2 Human and computer vision
1.3 The human vision system
1.3.1 The eye
1.3.2 The neural system
1.3.3 Processing
1.4 Computer vision systems
1.4.1 Cameras
1.4.2 Computer interfaces
1.4.3 Processing an image
1.5 Mathematical systems
1.5.1 Mathematical tools
1.5.2 Hello Matlab, hello images!
1.5.3 Hello Mathcad!
1.6 Associated literature
1.6.1 Journals, magazines, and conferences
1.6.2 Textbooks
1.6.3 The Web
1.7 Conclusions
1.8 References
CHAPTER 2 Images, Sampling, and Frequency
Domain Processing
2.1 Overview
2.2 Image formation
2.3 The Fourier transform
2.4 The sampling criterion
2.5 The discrete Fourier transform
2.5.1 1D transform
2.5.2 2D transform
2.6 Other properties of the Fourier transform
2.6.1 Shift invariance
2.6.2 Rotation
2.6.3 Frequency scaling
2.6.4 Superposition (linearity)
v
2.7 Transforms other than Fourier
2.7.1 Discrete cosine transform
2.7.2 Discrete Hartley transform
2.7.3 Introductory wavelets
2.7.4 Other transforms
2.8 Applications using frequency domain properties
2.9 Further reading
2.10 References
CHAPTER 3 Basic Image Processing Operations 83
3.1 Overview
3.2 Histograms
3.3 Point operators
3.3.1 Basic point operations
3.3.2 Histogram normalization
3.3.3 Histogram equalization
3.3.4 Thresholding
3.4 Group operations
3.4.1 Template convolution
3.4.2 Averaging operator
3.4.3 On different template size
3.4.4 Gaussian averaging operator
3.4.5 More on averaging
3.5 Other statistical operators
3.5.1 Median filter
3.5.2 Mode filter
3.5.3 Anisotropic diffusion
3.5.4 Force field transform
3.5.5 Comparison of statistical operators
3.6 Mathematical morphology
3.6.1 Morphological operators
3.6.2 Gray-level morphology
3.6.3 Gray-level erosion and dilation
3.6.4 Minkowski operators
3.7 Further reading
3.8 References
CHAPTER 4 Low-Level Feature Extraction (including
edge detection)
4.1 Overview
4.2 Edge detection
4.2.1 First-order edge-detection operators
4.2.2 Second-order edge-detection operators
vi Contents
4.2.3 Other edge-detection operators
4.2.4 Comparison of edge-detection operators
4.2.5 Further reading on edge detection
4.3 Phase congruency
4.4 Localized feature extraction
4.4.1 Detecting image curvature (corner extraction)
4.4.2 Modern approaches: region/patch analysis
4.5 Describing image motion
4.5.1 Area-based approach
4.5.2 Differential approach
4.5.3 Further reading on optical flow
4.6 Further reading
4.7 References
CHAPTER 5 High-Level Feature Extraction: Fixed Shape
Matching
5.1 Overview
5.2 Thresholding and subtraction
5.3 Template matching
5.3.1 Definition
5.3.2 Fourier transform implementation
5.3.3 Discussion of template matching
5.4 Feature extraction by low-level features
5.4.1 Appearance-based approaches
5.4.2 Distribution-based descriptors
5.5 Hough transform
5.5.1 Overview
5.5.2 Lines
5.5.3 HT for circles
5.5.4 HT for ellipses
5.5.5 Parameter space decomposition
5.5.6 Generalized HT
5.5.7 Other extensions to the HT
5.6 Further reading
5.7 References
CHAPTER 6 High-Level Feature Extraction: Deformable
Shape Analysis
6.1 Overview
6.2 Deformable shape analysis
6.2.1 Deformable templates
6.2.2 Parts-based shape analysis
Contents vii
6.3 Active contours (snakes)
6.3.1 Basics
6.3.2 The Greedy algorithm for snakes
6.3.3 Complete (Kass) snake implementation
6.3.4 Other snake approaches
6.3.5 Further snake developments
6.3.6 Geometric active contours (level-set-based
approaches)
6.4 Shape skeletonization
6.4.1 Distance transforms
6.4.2 Symmetry
6.5 Flexible shape models—active shape and active
appearance
6.6 Further reading
6
About the authors
CHAPTER 1 Introduction
1.1 Overview
1.2 Human and computer vision
1.3 The human vision system
1.3.1 The eye
1.3.2 The neural system
1.3.3 Processing
1.4 Computer vision systems
1.4.1 Cameras
1.4.2 Computer interfaces
1.4.3 Processing an image
1.5 Mathematical systems
1.5.1 Mathematical tools
1.5.2 Hello Matlab, hello images!
1.5.3 Hello Mathcad!
1.6 Associated literature
1.6.1 Journals, magazines, and conferences
1.6.2 Textbooks
1.6.3 The Web
1.7 Conclusions
1.8 References
CHAPTER 2 Images, Sampling, and Frequency
Domain Processing
2.1 Overview
2.2 Image formation
2.3 The Fourier transform
2.4 The sampling criterion
2.5 The discrete Fourier transform
2.5.1 1D transform
2.5.2 2D transform
2.6 Other properties of the Fourier transform
2.6.1 Shift invariance
2.6.2 Rotation
2.6.3 Frequency scaling
2.6.4 Superposition (linearity)
v
2.7 Transforms other than Fourier
2.7.1 Discrete cosine transform
2.7.2 Discrete Hartley transform
2.7.3 Introductory wavelets
2.7.4 Other transforms
2.8 Applications using frequency domain properties
2.9 Further reading
2.10 References
CHAPTER 3 Basic Image Processing Operations 83
3.1 Overview
3.2 Histograms
3.3 Point operators
3.3.1 Basic point operations
3.3.2 Histogram normalization
3.3.3 Histogram equalization
3.3.4 Thresholding
3.4 Group operations
3.4.1 Template convolution
3.4.2 Averaging operator
3.4.3 On different template size
3.4.4 Gaussian averaging operator
3.4.5 More on averaging
3.5 Other statistical operators
3.5.1 Median filter
3.5.2 Mode filter
3.5.3 Anisotropic diffusion
3.5.4 Force field transform
3.5.5 Comparison of statistical operators
3.6 Mathematical morphology
3.6.1 Morphological operators
3.6.2 Gray-level morphology
3.6.3 Gray-level erosion and dilation
3.6.4 Minkowski operators
3.7 Further reading
3.8 References
CHAPTER 4 Low-Level Feature Extraction (including
edge detection)
4.1 Overview
4.2 Edge detection
4.2.1 First-order edge-detection operators
4.2.2 Second-order edge-detection operators
vi Contents
4.2.3 Other edge-detection operators
4.2.4 Comparison of edge-detection operators
4.2.5 Further reading on edge detection
4.3 Phase congruency
4.4 Localized feature extraction
4.4.1 Detecting image curvature (corner extraction)
4.4.2 Modern approaches: region/patch analysis
4.5 Describing image motion
4.5.1 Area-based approach
4.5.2 Differential approach
4.5.3 Further reading on optical flow
4.6 Further reading
4.7 References
CHAPTER 5 High-Level Feature Extraction: Fixed Shape
Matching
5.1 Overview
5.2 Thresholding and subtraction
5.3 Template matching
5.3.1 Definition
5.3.2 Fourier transform implementation
5.3.3 Discussion of template matching
5.4 Feature extraction by low-level features
5.4.1 Appearance-based approaches
5.4.2 Distribution-based descriptors
5.5 Hough transform
5.5.1 Overview
5.5.2 Lines
5.5.3 HT for circles
5.5.4 HT for ellipses
5.5.5 Parameter space decomposition
5.5.6 Generalized HT
5.5.7 Other extensions to the HT
5.6 Further reading
5.7 References
CHAPTER 6 High-Level Feature Extraction: Deformable
Shape Analysis
6.1 Overview
6.2 Deformable shape analysis
6.2.1 Deformable templates
6.2.2 Parts-based shape analysis
Contents vii
6.3 Active contours (snakes)
6.3.1 Basics
6.3.2 The Greedy algorithm for snakes
6.3.3 Complete (Kass) snake implementation
6.3.4 Other snake approaches
6.3.5 Further snake developments
6.3.6 Geometric active contours (level-set-based
approaches)
6.4 Shape skeletonization
6.4.1 Distance transforms
6.4.2 Symmetry
6.5 Flexible shape models—active shape and active
appearance
6.6 Further reading
6
