Computer Vision: A Modern Approach, International Edition, 2nd edition

Published by Pearson Higher Education (February 14, 2012) © 2013

  • David A. Forsyth University of Illinois at Urbana-Champaign
  • Jean Ponce Ecole Normale Superieure, Paris
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Title overview

  • Broad coverage—Coverage of a wide range of topics allows customization to fit instructor, student, and course needs.
    • Allows instructors to select the most relevant topics for their students and encourages students to enrich their coursework by reading information on other computer vision topics.
  • Most comprehensive and up-to-date text on computer vision—Includes essential topics that either reflect practical significance or are of theoretical importance.
    • Provides students with the most coherent synthesis of current views and teaches them successful techniques for building applications.
  • Depth of the material accessible to various levels of students—Topics are discussed in substantial and increasing depth.
    • While the first half of each chapter is accessible to undergraduates, a good grasp of each chapter provides students with a professional level of skill and knowledge.
  • Application surveys—Describe numerous important application areas such as image based rendering and digital libraries.
    • Teaches students about practical use of techniques and helps them gain insight into the demands of applications.
  • Many important algorithms broken down and illustrated in pseudo code.
    • Enables students to build working systems easily as they can understand the construction of the final application.
  • Excellent pedagogy throughout the text—Includes numerous worked examples, exercises, programming assignments, and extensive illustrations.
    • Provides students with ample opportunity to apply the concepts in the text.

Table of contents

I IMAGE FORMATION 1
1 Geometric Camera Models 3
1.1 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 8
1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.2 Intrinsic and Extrinsic Parameters . . . . . . . . . . . . . . . . . . . 14
1.2.1 Rigid Transformations and Homogeneous Coordinates . . . . 14
1.2.2 Intrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 16
1.2.3 Extrinsic Parameters . . . . . . . . . . . . . . . . . . . . . . . 18
1.2.4 Perspective Projection Matrices . . . . . . . . . . . . . . . . . 19
1.2.5 Weak-Perspective Projection Matrices . . . . . . . . . . . . . 20
1.3 Geometric Camera Calibration . . . . . . . . . . . . . . . . . . . . . 22
1.3.1 ALinear Approach to Camera Calibration . . . . . . . . . . . 23
1.3.2 ANonlinear Approach to Camera Calibration . . . . . . . . . 27
1.4 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 Light and Shading 32
2.1 Modelling Pixel Brightness . . . . . . . . . . . . . . . . . . . . . . . 32
2.1.1 Reflection at Surfaces . . . . . . . . . . . . . . . . . . . . . . 33
2.1.2 Sources and Their Effects . . . . . . . . . . . . . . . . . . . . 34
2.1.3 The Lambertian+Specular Model . . . . . . . . . . . . . . . . 36
2.1.4 Area Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2 Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2.1 Radiometric Calibration and High Dynamic Range Images . . 38
2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . 40
2.2.3 Inferring Lightness and Illumination . . . . . . . . . . . . . . 43
2.2.4 Photometric Stereo: Shape from Multiple Shaded Images . . 46
2.3 Modelling Interreflection . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.3.1 The Illumination at a Patch Due to an Area Source . . . . . 52
2.3.2 Radiosity and Exitance . . . . . . . . . . . . . . . . . . . . . 54
2.3.3 An Interreflection Model . . . . . . . . . . . . . . . . . . . . . 55
2.3.4 Qualitative Properties of Interreflections . . . . . . . . . . . . 56
2.4 Shape from One Shaded Image . . . . . . . . . . . . . . . . . . . . . 59
2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3 Color 68
3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1.2 Color Receptors . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
3.2.1 The Color of Light Sources . . . . . . . . . . . . . . . . . . . 73
3.2.2 The Color of Surfaces . . . . . . . . . . . . . . . . . . . . . . 76
3.3 Representing Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.3.1 Linear Color Spaces . . . . . . . . . . . . . . . . . . . . . . . 77
3.3.2 Non-linear Color Spaces . . . . . . . . . . . . . . . . . . . . . 83
3.4 AModel of Image Color . . . . . . . . . . . . . . . . . . . . . . . . . 86
3.4.1 The Diffuse Term . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.4.2 The Specular Term . . . . . . . . . . . . . . . . . . . . . . . . 90
3.5 Inference from Color . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.5.1 Finding Specularities Using Color . . . . . . . . . . . . . . . 90
3.5.2 Shadow Removal Using Color . . . . . . . . . . . . . . . . . . 92
3.5.3 Color Constancy: Surface Color from Image Color . . . . . . 95
3.6 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
II EARLY VISION: JUST ONE IMAGE 105
4 Linear Filter

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