計算機視覺是研究如何使人工系統從圖像或多維數據中“感知”的科學。本書是計算機視覺領域的經典教材，內容涉及幾何攝像模型、光照和著色、色彩、線性濾波、局部圖像特征、紋理、立體相對、運動結構、聚類分割、組合與模型擬合、追蹤、配準、平滑表面與骨架、距離數據、圖像分類、對象檢測與識別、基于圖像的建模與渲染、人形研究、圖像搜索與檢索、優化技術等內容。與前一版相比，本書簡化了部分主題，增加了應用示例，重寫了關于現代特性的內容，詳述了現代圖像編輯技術與對象識別技術。
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 ALinear Approach to Camera Calibration
1.3.2 ANonlinear 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 Illumination 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 AModel 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
3.6 Notes
II EARLY VISION: JUST ONE IMAGE
4 Linear Filters 107
4.1 Linear Filters and Convolution
4.1.1 Convolution
4.2 Shift Invariant Linear Systems
4.2.1 Discre
Inference from Shading
Registered images are not essential for radiometric calibration. For example， it is sufficient to have two images where we believe the histogram of Eij values is the same （Grossberg and Nayar 2002）. This occurs， for example， when the images are of the same scene， but are not precisely registered. Patterns of intensity around edges also can reveal calibration （Lin et al. 2004）.
There has not been much recent study of lightness constancy algorithms. The basic idea is due to Land and McCann （1971）.Their work was formalized for the computer vision community by Horn （1974）. A variation on Horn's algorithm was constructed by Blake （1985）. This is the lightness algorithm we describe. It appeared originally in a slightly different form， where it was called the Retinex algorithm （Land and McCann 1971）. Retinex was originally intended as a color constancy algorithm. It is surprisingly difficult to analyze （Brainard and Wandell 1986）.
Retinex estimates the log-illumination term by subtracting the log-albedo from the log-intensity. This has the disadvantage that we do not impose any struc- tural eonstraints on illumination. This point has largely been ignored， beeause the main focus has been on albedo estimates. However， albedo estimates are likely to be improved by balancing violations of albedo eonstraints with those of illumination constraints.
Lightness techniques are not as widely used as they should be， particularly given that there is some evidence that they produce useful information on real images （Brelstaff and Blake 1987）. Classifying illumination versus albedo simply by looking at the magnitude of the gradient is crude， and ignores important cues. Sharp shading changes occur at shadow boundaries or normal discontinuities， but using chromaticity （Funt et al. 1992） or multiple images under different lighting conditions （Weiss 20011 yields improved estimates. One can learn to distinguish illumination from albedo （Freeman et al. 2000）. Discriminative methods to classify edges into albedo or shading help （Tappen et al. 2006b） and chromaticity cues can contribute （Farenzena and Fusiello 2007）.
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