《計算機視覺：一種現代的方法》是2004年清華大學出版社出版的圖書，作者是Forsyth, Ponce。
基本介紹
- 書名：計算機視覺：一種現代的方法
- 作者：Forsyth, Ponce
- ISBN：9787302077954
- 定價：65元
- 出版社：清華大學出版社
- 出版時間：2004.02.01
內容簡介,目錄,
內容簡介
本書是由計算機視覺領域的兩位權威專家編寫的，全面介紹了現代計算機視覺的各種研究方法。本書不僅系統闡述了計算機視覺的原理與方法，而且還給出了很多有用的資料，如偽代碼、工作範例、練習以及編程作業等，以助於讀者創建自己的應用程式。通過本書的學習，讀者可以掌握來自作者第一手的計算機處理視覺技術以及大量的數學方法。本書是計算機科學、計算機工程及電子工程高年級本科生和研究生“計算機視學”的很好教材，也是從事計算機視覺研究人員的重要參考書。
目錄
Part I Image Formation and Image Models
1 CAMERAS
1.1 Pinhole Cameras
1.1.1 Perspective Projection
1.1.2 Affine Projection
1.2 Cameras with Lenses
1.2.1 Paraxial Geometric Optics
1.2.2 Thin Lenses
1.2.3 Real Lenses
1.3 The Human Eye
1.4 Sensing
1.4.1 CCD Cameras
1.4.2 Sensor Models
1.5 Notes
Problems
2 GEOMETRIC CAMERA MODELS
2.1 Elements of analytical Euclidean Geometry
2.1.1 Coordinate Systems and Homogeneous Coordinates
2.1.2 Coordinate System Changes and Rigid Transformations
2.2 Camera Parameters and the Perspective Projection
2.2.1 Intrinsic Parameters
2.2.2 Extrinsic Parameters
2.2.3 A Characterization of Perspective Projection Matrices
2.3 Affine Cameras and Affine Projection Equations
2.3.1 Affine Cameras
2.3.2 Affine Projection Equations
2.3.3 A Characterization of Affine Projection Matrices
2.4 Notes
Problems
3 GEOMETRIC CAMERA CALIBRATION
3.1 Least-Squares Parameter Estimation
3.1.1 Linear Least-Squares Methods
3.1.2 Nonlinear Least-Squares Methods
3.2 A Linear Approach to Camera Calibration
3.2.1 Estimation of the Projection Matrix
3.2.2 Estimation of the Intrinsic and Extrinsic Parameters
3.2.3 Degenerate Point Configurations
3.3 Taking Radial Distortion into Account
3.3.1 Estimation of the Projection Matrix
3.3.2 Estimation of the Intrinsic and Extrinsic Parameters
3.3.3 Degenerate Point Configurations
3.4 Analytical Photogrammetry
3.5 An Application:Mobile Robot Localization
3.6 Notes
Problems
4 RADIOMETRY-MEASURING LIGHT
4.1 Light in Space
4.1.1 Foreshortening
4.1.2 Solid Angle
4.1.3 Radiance
4.2 Light at Surfaces
4.2.1 Simplifying Assumptions
4.2.2 The Bidirectional Reflectance Distribution Function
4.2.3 Example:The Radiometry of Thin Lenses
4.3 Important Special Cases
4.3.1 Radiosity
4.3.2 directional Hemispheric Reflectance
4.3.3 Lambertian Surfaces and Albedo
4.3.4 Specular Surfaces
4.3.5 The Lambertian+Specular Model
4.4 Notes
Problems
5 SOURCES,SHADOWS,AND SHADING
6 COLOR
Part II Early Vision:Just One Image
7 LINEAR FILTERS
8 EDGE DETECTION
9 TEXTURE
Part III Early Vision:Multiple Images
10 THE GEOMETRY OF MULTIPLE VIEWS
11 STEREOPSIS
12 AFFINE STRUCTURE FROM MOTION
13 PROJECTIVE STRUCTURE FROM MOTION
Part IV Mid-Level Vision
14 SEGMENTATION BY CLUSTERING
15 SEGMENTATION BY FITTING A MODEL
16 SEGMENTATION AN FITTING USING PROBABILISTIC METHODS
17 TRACKING WITH LINEAR DYNAMIC MODELS
Part Ⅴ High-Level Vision:Geometric Methods
Part Ⅵ High-Level Vision:Probabilistic and Inferential Methods
Part Ⅶ Applications
BIBLIOGRAPHY
INDEX
1 CAMERAS
1.1 Pinhole Cameras
1.1.1 Perspective Projection
1.1.2 Affine Projection
1.2 Cameras with Lenses
1.2.1 Paraxial Geometric Optics
1.2.2 Thin Lenses
1.2.3 Real Lenses
1.3 The Human Eye
1.4 Sensing
1.4.1 CCD Cameras
1.4.2 Sensor Models
1.5 Notes
Problems
2 GEOMETRIC CAMERA MODELS
2.1 Elements of analytical Euclidean Geometry
2.1.1 Coordinate Systems and Homogeneous Coordinates
2.1.2 Coordinate System Changes and Rigid Transformations
2.2 Camera Parameters and the Perspective Projection
2.2.1 Intrinsic Parameters
2.2.2 Extrinsic Parameters
2.2.3 A Characterization of Perspective Projection Matrices
2.3 Affine Cameras and Affine Projection Equations
2.3.1 Affine Cameras
2.3.2 Affine Projection Equations
2.3.3 A Characterization of Affine Projection Matrices
2.4 Notes
Problems
3 GEOMETRIC CAMERA CALIBRATION
3.1 Least-Squares Parameter Estimation
3.1.1 Linear Least-Squares Methods
3.1.2 Nonlinear Least-Squares Methods
3.2 A Linear Approach to Camera Calibration
3.2.1 Estimation of the Projection Matrix
3.2.2 Estimation of the Intrinsic and Extrinsic Parameters
3.2.3 Degenerate Point Configurations
3.3 Taking Radial Distortion into Account
3.3.1 Estimation of the Projection Matrix
3.3.2 Estimation of the Intrinsic and Extrinsic Parameters
3.3.3 Degenerate Point Configurations
3.4 Analytical Photogrammetry
3.5 An Application:Mobile Robot Localization
3.6 Notes
Problems
4 RADIOMETRY-MEASURING LIGHT
4.1 Light in Space
4.1.1 Foreshortening
4.1.2 Solid Angle
4.1.3 Radiance
4.2 Light at Surfaces
4.2.1 Simplifying Assumptions
4.2.2 The Bidirectional Reflectance Distribution Function
4.2.3 Example:The Radiometry of Thin Lenses
4.3 Important Special Cases
4.3.1 Radiosity
4.3.2 directional Hemispheric Reflectance
4.3.3 Lambertian Surfaces and Albedo
4.3.4 Specular Surfaces
4.3.5 The Lambertian+Specular Model
4.4 Notes
Problems
5 SOURCES,SHADOWS,AND SHADING
6 COLOR
Part II Early Vision:Just One Image
7 LINEAR FILTERS
8 EDGE DETECTION
9 TEXTURE
Part III Early Vision:Multiple Images
10 THE GEOMETRY OF MULTIPLE VIEWS
11 STEREOPSIS
12 AFFINE STRUCTURE FROM MOTION
13 PROJECTIVE STRUCTURE FROM MOTION
Part IV Mid-Level Vision
14 SEGMENTATION BY CLUSTERING
15 SEGMENTATION BY FITTING A MODEL
16 SEGMENTATION AN FITTING USING PROBABILISTIC METHODS
17 TRACKING WITH LINEAR DYNAMIC MODELS
Part Ⅴ High-Level Vision:Geometric Methods
Part Ⅵ High-Level Vision:Probabilistic and Inferential Methods
Part Ⅶ Applications
BIBLIOGRAPHY
INDEX
