《国外计算机科学教材系列·计算机视觉:一种现代方法(第二版)(英文版)》 戴维·A.福赛斯 (David A.Forsyth), 简·泊斯 (Jean Ponce) 9787121318269
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编辑推荐
*数学知识简洁，清晰
*关于现代特征的内容
*现代图像编辑技术以及物体识别技术
作者简介
David Forsyth：1984年于威特沃特斯兰德大学取得了电气工程学士学位，1986年取得电气工程硕士学位，1989年于牛津贝列尔学院取得博士学位。之后在艾奥瓦大学任教3年，在加州大学伯克利分校任教10年，再后在伊利诺伊大学任教。2000年和2001年任IEEE计算机视觉与模式识别会议(CVPR)执行副主席，2006年任CVPR常任副主席，2008年任欧洲计算机视觉会议执行副主席，是所有关于计算机视觉主要国际会议的常任执委会成员。他为SIGGRAPH执委会工作了5期。2006年获IEEE技术成就奖，2009年成为IEEE会士。
Jean Ponce：于1988年在巴黎奥赛大学获得计算机科学博士学位。1990年至2005年，作为研究科学家分别供职于法国国家信息研究所、麻省理工学院人工智能实验室和斯坦福大学机器人实验室；1990年至2005年，供职于伊利诺伊大学计算机科学系。2005年开始，成为法国巴黎高等师范学校教授。
目录
Ⅰ IMAGE FORMATION
1 Geometric Camera Models
1.1 Image Formation
1.1.1 Pinhole Perspective
1.1.2 Weak Perspective
1.1.3 Cameras with Lenses
1.1.4 The Human Eye
1.2 Intrinsic and Extrinsic Parameters
1.2.1 Rigid Transformations and Homogeneous Coordinates
1.2.2 Intrinsic Parameters
1.2.3 Extrinsic Parameters
1.2.4 Perspective Projection Matrices
1.2.5 Weak—Perspective Projection Matrices
1.3 Geometric Camera Calibration
1.3.1 A Linear Approach to Camera Calibration
1.3.2 A Nonlinear Approach to Camera Calibration
1.4 Notes
2 Light and Shading
2.1 Modelling Pixel Brightness
2.1.1 Reflection at Surfaces
2.1.2 Sources and Their Effects
2.1.3 The Lambertian+Specular Model
2.1.4 Area Sources
2.2 Inference from Shading
2.2.1 Radiometric Calibration and High Dynamic Range Images
2.2.2 The Shape of Specularities
2.2.3 Inferring Lightness and Illumination
2.2.4 Photometric Stereo： Shape from Multiple Shaded Images
2.3 Modelling Interreflection
2.3.1 The Illurrunation at a Patch Due to an Area Source
2.3.2 Radiosity and Exitance
2.3.3 An Interreflection Model
2.3.4 Qualitative Properties of Interreflections
2.4 Shape from One Shaded Image
2.5 Notes
3 Color
3.1 Human Color Perception
3.1.1 Color Matching
3.1.2 Color Receptors
3.2 The Physics of Color
3.2.1 The Color of Light Sources
3.2.2 The Color of Surfaces
3.3 Representing Color
3.3.1 Linear Color Spaces
3.3.2 Non—linear Color Spaces
3.4 A Model of Image Color
3.4.1 The Diffuse Term
3.4.2 The Specular Term
3.5 Inference from Color
3.5.1 Finding Specularities Using Color
3.5.2 Shadow Removal Using Color
3.5.3 Color Constancy： Surface Color from Image Color
Ⅱ EARLY VISION： JUST ONE IMAGE
4 Linear Filters
4.1 Linear Filters and Convolution
4.1.1 Convolution
4.2 Shift Invariant Linear Systems
4.2.1 Discrete Convolution
4.2.2 Continuous Convolution
4.2.3 Edge Effects in Discrete Convolutions
4.3 Spatial Frequency and Fourier Transforms
4.3.1 Fourier Transforms
4.4 Sampling and Aliasing
4.4.1 Sampling
4.4.2 Aliasing
4.4.3 Smoothing and Resampling
4.5 Filters as Templates
4.5.1 Convolution as a Dot Product
4.5.2 Changing Basis
4.6 Technique： Normalized Correlation and Finding Patterns
4.6.1 Controlling the Television by Finding Hands by Normalized Correlation
4.7 Technique： Scale and Image Pyramids
4.7.1 The Gaussian Pyramid
4.7.2 Applications of Scaled Representations
5 Local Image Features
5.1 Computing the Image Gradient
5.1.1 Derivative of Gaussian Filters
5.2 Representing the Image Gradient
5.2.1 Gradient—Based Edge Detectors
5.3 Finding Corners and Building Neighborhoods
5.3.1 Finding Corners
5.3.2 Using Scale and Orientation to Build a Neighborhood
5.4 Describing Neighborhoods with SIFT and HOG Features
5.4.1 SIFT Features
5.4.2 HOG Features
5.5 Computing Local Features in Practice
6 Texture
6.1 Local Texture Representations Using Filters
6.1.1 Spots and Bars
6.1.2 From Filter Outputs to Texture Representation
6.1.3 Local Texture Representations in Practice
6.2 Pooled Texture Representations by Discovering Textons
6.2.1 Vector Quantization and Textons
6.2.2 K—means Clustering for Vector Quantization
6.3 Synthesizing Textures and Filling Holes in Images
6.3.1 Synthesis by Sampling Local Models
6.3.2 Filling in Holes in Images
6.4 Image Denoising
6.4.1 Non—local Means
6.4.2 Block Matching 3D （BM3D）
6.4.3 Learned Sparse Coding
6.5 Shape from Texture
6.5.1 Shape from Texture for Planes
6.5.2 Shape from Texture for Curved Surfaces
Ⅲ EARLY VISION： MULTIPLE IMAGES
7 Stereopsis
7.1 Binocular Camera Geometry and the Epipolar Constraint
7.1.1 Epipolar Geometry
7.1.2 The Essential Matrix
7.1.3 The Fundamental Matrix
7.2 Binocular Reconstruction
7.2.1 Image Rectification
7.3 Human Stereopsis
7.4 Local Methods for Binocular Fusion
7.4.1 Correlation
7.4.2 Multi—Scale Edge Matching
7.5 Global Methods for Binocular Fusion
7.5.1 Ordering Constraints and Dynamic Programming
7.5.2 Smoothness and Graphs
7.6 Using More Cameras
7.7 Application： Robot Navigation
8 Structure from Motion
8.1 Internally Calibrated Perspective Cameras
8.1.1 Natural Ambiguity of the Problem
8.1.2 Euclidean Structure and Motion from Two Images
8.1.3 Euclidean Structure and Motion from Multiple Images
8.2 Uncalibrated Weak—Perspective Cameras
8.2.1 Natural Ambiguity of the Problem
8.2.2 Affine Structure and Motion from Two Images
8.2.3 Affine Structure and Motion from Multiple Images
8.2.4 From Affine to Euclidean Shape
8.3 Uncalibrated Perspective Cameras
8.3.1 Natural Ambiguity of the Problem
8.3.2 Projective Structure and Motion from Two Images
8.3.3 Projective Structure and Motion from Multiple Images
8.3.4 From Projective to Euclidean Shape
……
Ⅳ MID—LEVEL VISION
Ⅴ HIGH—LEVEL VISION
Ⅵ APPLICATIONS AND TOPICS
Ⅶ BACKGROUND MATERIAL
Index
List of Algorithms
Bibliography
文摘
版权页：
插图：
The sensitivities of the three different kinds of receptor to different wavelengths can be obtained by comparing color matching data for normal observers with color matching data for observers lacking one type of cone.Sensitivities obtained in this fashion are shown in Figure 3.3.The three types of cone are properly called S cones， M cones， and L cones （for their peak sensitivity being to short—，medium—， and long—wavelength light， respectively）.They are occasionally called blue， green， and red cones； however， this is bad practice， because the sensation of red is definitely not caused by the stimulation of red cones， and so on.
3.2 THE PHYSICS OF COLOR
Several different mechanisms result in colored light.First， light sources can produce different amounts of light at different wavelengths.This is what makes incandescentlights look orange or yellow， and fluorescent lights look bluish.Second， for mostdiffuse surfaces， albedo depends on wavelength， so that some wavelengths may be largely absorbed and others largely reflected.
*数学知识简洁，清晰
*关于现代特征的内容
*现代图像编辑技术以及物体识别技术
作者简介
David Forsyth：1984年于威特沃特斯兰德大学取得了电气工程学士学位，1986年取得电气工程硕士学位，1989年于牛津贝列尔学院取得博士学位。之后在艾奥瓦大学任教3年，在加州大学伯克利分校任教10年，再后在伊利诺伊大学任教。2000年和2001年任IEEE计算机视觉与模式识别会议(CVPR)执行副主席，2006年任CVPR常任副主席，2008年任欧洲计算机视觉会议执行副主席，是所有关于计算机视觉主要国际会议的常任执委会成员。他为SIGGRAPH执委会工作了5期。2006年获IEEE技术成就奖，2009年成为IEEE会士。
Jean Ponce：于1988年在巴黎奥赛大学获得计算机科学博士学位。1990年至2005年，作为研究科学家分别供职于法国国家信息研究所、麻省理工学院人工智能实验室和斯坦福大学机器人实验室；1990年至2005年，供职于伊利诺伊大学计算机科学系。2005年开始，成为法国巴黎高等师范学校教授。
目录
Ⅰ IMAGE FORMATION
1 Geometric Camera Models
1.1 Image Formation
1.1.1 Pinhole Perspective
1.1.2 Weak Perspective
1.1.3 Cameras with Lenses
1.1.4 The Human Eye
1.2 Intrinsic and Extrinsic Parameters
1.2.1 Rigid Transformations and Homogeneous Coordinates
1.2.2 Intrinsic Parameters
1.2.3 Extrinsic Parameters
1.2.4 Perspective Projection Matrices
1.2.5 Weak—Perspective Projection Matrices
1.3 Geometric Camera Calibration
1.3.1 A Linear Approach to Camera Calibration
1.3.2 A Nonlinear Approach to Camera Calibration
1.4 Notes
2 Light and Shading
2.1 Modelling Pixel Brightness
2.1.1 Reflection at Surfaces
2.1.2 Sources and Their Effects
2.1.3 The Lambertian+Specular Model
2.1.4 Area Sources
2.2 Inference from Shading
2.2.1 Radiometric Calibration and High Dynamic Range Images
2.2.2 The Shape of Specularities
2.2.3 Inferring Lightness and Illumination
2.2.4 Photometric Stereo： Shape from Multiple Shaded Images
2.3 Modelling Interreflection
2.3.1 The Illurrunation at a Patch Due to an Area Source
2.3.2 Radiosity and Exitance
2.3.3 An Interreflection Model
2.3.4 Qualitative Properties of Interreflections
2.4 Shape from One Shaded Image
2.5 Notes
3 Color
3.1 Human Color Perception
3.1.1 Color Matching
3.1.2 Color Receptors
3.2 The Physics of Color
3.2.1 The Color of Light Sources
3.2.2 The Color of Surfaces
3.3 Representing Color
3.3.1 Linear Color Spaces
3.3.2 Non—linear Color Spaces
3.4 A Model of Image Color
3.4.1 The Diffuse Term
3.4.2 The Specular Term
3.5 Inference from Color
3.5.1 Finding Specularities Using Color
3.5.2 Shadow Removal Using Color
3.5.3 Color Constancy： Surface Color from Image Color
Ⅱ EARLY VISION： JUST ONE IMAGE
4 Linear Filters
4.1 Linear Filters and Convolution
4.1.1 Convolution
4.2 Shift Invariant Linear Systems
4.2.1 Discrete Convolution
4.2.2 Continuous Convolution
4.2.3 Edge Effects in Discrete Convolutions
4.3 Spatial Frequency and Fourier Transforms
4.3.1 Fourier Transforms
4.4 Sampling and Aliasing
4.4.1 Sampling
4.4.2 Aliasing
4.4.3 Smoothing and Resampling
4.5 Filters as Templates
4.5.1 Convolution as a Dot Product
4.5.2 Changing Basis
4.6 Technique： Normalized Correlation and Finding Patterns
4.6.1 Controlling the Television by Finding Hands by Normalized Correlation
4.7 Technique： Scale and Image Pyramids
4.7.1 The Gaussian Pyramid
4.7.2 Applications of Scaled Representations
5 Local Image Features
5.1 Computing the Image Gradient
5.1.1 Derivative of Gaussian Filters
5.2 Representing the Image Gradient
5.2.1 Gradient—Based Edge Detectors
5.3 Finding Corners and Building Neighborhoods
5.3.1 Finding Corners
5.3.2 Using Scale and Orientation to Build a Neighborhood
5.4 Describing Neighborhoods with SIFT and HOG Features
5.4.1 SIFT Features
5.4.2 HOG Features
5.5 Computing Local Features in Practice
6 Texture
6.1 Local Texture Representations Using Filters
6.1.1 Spots and Bars
6.1.2 From Filter Outputs to Texture Representation
6.1.3 Local Texture Representations in Practice
6.2 Pooled Texture Representations by Discovering Textons
6.2.1 Vector Quantization and Textons
6.2.2 K—means Clustering for Vector Quantization
6.3 Synthesizing Textures and Filling Holes in Images
6.3.1 Synthesis by Sampling Local Models
6.3.2 Filling in Holes in Images
6.4 Image Denoising
6.4.1 Non—local Means
6.4.2 Block Matching 3D （BM3D）
6.4.3 Learned Sparse Coding
6.5 Shape from Texture
6.5.1 Shape from Texture for Planes
6.5.2 Shape from Texture for Curved Surfaces
Ⅲ EARLY VISION： MULTIPLE IMAGES
7 Stereopsis
7.1 Binocular Camera Geometry and the Epipolar Constraint
7.1.1 Epipolar Geometry
7.1.2 The Essential Matrix
7.1.3 The Fundamental Matrix
7.2 Binocular Reconstruction
7.2.1 Image Rectification
7.3 Human Stereopsis
7.4 Local Methods for Binocular Fusion
7.4.1 Correlation
7.4.2 Multi—Scale Edge Matching
7.5 Global Methods for Binocular Fusion
7.5.1 Ordering Constraints and Dynamic Programming
7.5.2 Smoothness and Graphs
7.6 Using More Cameras
7.7 Application： Robot Navigation
8 Structure from Motion
8.1 Internally Calibrated Perspective Cameras
8.1.1 Natural Ambiguity of the Problem
8.1.2 Euclidean Structure and Motion from Two Images
8.1.3 Euclidean Structure and Motion from Multiple Images
8.2 Uncalibrated Weak—Perspective Cameras
8.2.1 Natural Ambiguity of the Problem
8.2.2 Affine Structure and Motion from Two Images
8.2.3 Affine Structure and Motion from Multiple Images
8.2.4 From Affine to Euclidean Shape
8.3 Uncalibrated Perspective Cameras
8.3.1 Natural Ambiguity of the Problem
8.3.2 Projective Structure and Motion from Two Images
8.3.3 Projective Structure and Motion from Multiple Images
8.3.4 From Projective to Euclidean Shape
……
Ⅳ MID—LEVEL VISION
Ⅴ HIGH—LEVEL VISION
Ⅵ APPLICATIONS AND TOPICS
Ⅶ BACKGROUND MATERIAL
Index
List of Algorithms
Bibliography
文摘
版权页：
插图：
The sensitivities of the three different kinds of receptor to different wavelengths can be obtained by comparing color matching data for normal observers with color matching data for observers lacking one type of cone.Sensitivities obtained in this fashion are shown in Figure 3.3.The three types of cone are properly called S cones， M cones， and L cones （for their peak sensitivity being to short—，medium—， and long—wavelength light， respectively）.They are occasionally called blue， green， and red cones； however， this is bad practice， because the sensation of red is definitely not caused by the stimulation of red cones， and so on.
3.2 THE PHYSICS OF COLOR
Several different mechanisms result in colored light.First， light sources can produce different amounts of light at different wavelengths.This is what makes incandescentlights look orange or yellow， and fluorescent lights look bluish.Second， for mostdiffuse surfaces， albedo depends on wavelength， so that some wavelengths may be largely absorbed and others largely reflected.
| ISBN | 9787121318269 |
|---|---|
| 出版社 | 电子工业出版社 |
| 作者 | 戴维·A.福赛斯 (David A.Forsyth) |
| 尺寸 | 16 |