本书是由计算机视觉领域的两位权威专家编写的，全面介绍了现代计算机视觉的各种研究方法。本书不仅系统阐述了计算机视觉的原理与方法，而且还给出了很多有用的资料，如伪代码、工作範例、练习以及编程作业等，以助于读者创建自己的应用程式。通过本书的学习，读者可以掌握来自作者第一手的计算机处理视觉技术以及大量的数学方法。本书是计算机科学、计算机工程及电子工程高年级本科生和研究生“计算机视学”的很好教材，也是从事计算机视觉研究人员的重要参考书。

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