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Computer.Vision.A.Modern.Approach,.David.A..Forsyth,.Jean.Ponce,.2ed.pdf

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    Cover
    Computer Vision: A Modern Approach
    ©
    Dedication Page
    Contents
    Preface
    I: IMAGE FORMATION
    1 Geometric Camera Models
    1.1 Image Formation
    1.2 Intrinsic and Extrinsic Parameters
    1.3 Geometric Camera Calibration
    1.4 Notes
    2 Light and Shading
    2.1 Modelling Pixel Brightness
    2.2 Inference from Shading
    2.3 Modelling Interreflection
    2.4 Shape from One Shaded Image
    2.5 Notes
    3 Color
    3.1 Human Color Perception
    3.2 The Physics of Color
    3.3 Representing Color
    3.4 A Model of Image Color
    3.5 Inference from Color
    3.6 Notes
    II: EARLY VISION: JUST ONE IMAGE
    4 Linear Filters
    4.1 Linear Filters and Convolution
    4.2 Shift Invariant Linear Systems
    4.3 Spatial Frequency and Fourier Transforms
    4.4 Sampling and Aliasing
    4.5 Filters as Templates
    4.6 Technique: Normalized Correlation and Finding Patterns
    4.7 Technique: Scale and Image Pyramids
    4.8 Notes
    5 Local Image Features
    5.1 Computing the Image Gradient
    5.2 Representing the Image Gradient
    5.3 Finding Corners and Building Neighborhoods
    5.4 Describing Neighborhoods with SIFT and HOG Features
    5.5 Computing Local Features in Practice
    5.6 Notes
    6 Texture
    6.1 Local Texture Representations Using Filters
    6.2 Pooled Texture Representations by Discovering Textons
    6.3 Synthesizing Textures and Filling Holes in Images
    6.4 Image Denoising
    6.5 Shape from Texture
    6.6 Notes
    III: EARLY VISION: MULTIPLE IMAGES
    7 Stereopsis
    7.1 Binocular Camera Geometry and the Epipolar Constraint
    7.2 Binocular Reconstruction
    7.3 Human Stereopsis
    7.4 Local Methods for Binocular Fusion
    7.5 Global Methods for Binocular Fusion
    7.6 Using More Cameras
    7.7 Application: Robot Navigation
    7.8 Notes
    8 Structure from Motion
    8.1 Internally Calibrated Perspective Cameras
    8.2 Uncalibrated Weak-Perspective Cameras
    8.3 Uncalibrated Perspective Cameras
    8.4 Notes
    IV: MID-LEVEL VISION
    9 Segmentation by Clustering
    9.1 Human Vision: Grouping and Gestalt
    9.2 Important Applications
    9.3 Image Segmentation by Clustering Pixels
    9.4 Segmentation, Clustering, and Graphs
    9.5 Image Segmentation in Practice
    9.6 Notes
    10 Grouping and Model Fitting
    10.1 The Hough Transform
    10.2 Fitting Lines and Planes
    10.3 Fitting Curved Structures
    10.4 Robustness
    10.5 Fitting Using Probabilistic Models
    10.6 Motion Segmentation by Parameter Estimation
    10.7 Model Selection: WhichModel Is the Best Fit?
    10.8 Notes
    11 Tracking
    11.1 Simple Tracking Strategies
    11.2 Tracking Using Matching
    11.3 Tracking Linear Dynamical Models with Kalman Filters
    11.4 Data Association
    11.5 Particle Filtering
    11.6 Notes
    V: HIGH-LEVEL VISION
    12 Registration
    12.1 Registering Rigid Objects
    12.2 Model-based Vision: Registering Rigid Objects with Projection
    12.3 Registering Deformable Objects
    12.4 Notes
    13 Smooth Surfaces and Their Outlines
    13.1 Elements of Differential Geometry
    13.2 Contour Geometry
    13.3 Visual Events: More Differential Geometry
    13.4 Notes
    14 Range Data
    14.1 Active Range Sensors
    14.2 Range Data Segmentation
    14.3 Range Image Registration and Model Acquisition
    14.4 Object Recognition
    14.5 Kinect
    14.6 Notes
    15 Learning to Classify
    15.1 Classification, Error, and Loss
    15.2 Major Classification Strategies
    15.3 Practical Methods for Building Classifiers
    15.4 Notes
    16 Classifying Images
    16.1 Building Good Image Features
    16.2 Classifying Images of Single Objects
    16.3 Image Classification in Practice
    16.4 Notes
    17 Detecting Objects in Images
    17.1 The Sliding Window Method
    17.2 Detecting Deformable Objects
    17.3 The State of the Art of Object Detection
    17.4 Notes
    18 Topics in Object Recognition
    18.1 What Should Object Recognition Do?
    18.2 Feature Questions
    18.3 Geometric Questions
    18.4 Semantic Questions
    VI: APPLICATIONS AND TOPICS
    19 Image-Based Modeling and Rendering
    19.1 Visual Hulls
    19.2 Patch-Based Multi-View Stereopsis
    19.3 The Light Field
    19.4 Notes
    20 Looking at People
    20.1 HMM’s, Dynamic Programming, and Tree-Structured Models
    20.2 Parsing People in Images
    20.3 Tracking People
    20.4: 3D from2D: Lifting
    20.5 Activity Recognition
    20.6 Resources
    20.7 Notes
    21 Image Search and Retrieval
    21.1 The Application Context
    21.2 Basic Technologies from Information Retrieval
    21.3 Images as Documents
    21.4 Predicting Annotations for Pictures
    21.5 The State of the Art of Word Prediction
    21.6 Notes
    VII: BACKGROUND MATERIAL
    22 Optimization Techniques
    22.1 Linear Least-Squares Methods
    22.2 Nonlinear Least-Squares Methods
    22.3 Sparse Coding and Dictionary Learning
    22.4 Min-Cut/Max-Flow Problems and Combinatorial Optimization
    22.5 Notes
    Bibliography
    Index
    A
    B
    C
    D
    E
    F
    G
    H
    I
    J
    K
    L
    M
    N
    O
    P
    Q
    R
    S
    T
    U
    V
    W
    Y
    Z
    List of Algorithms
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    COMPUTER VISION A MODERN APPROACH second edition David A. Forsyth University of Illinois at Urbana-Champaign Jean Ponce Ecole Normale Supérieure Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montreal Toronto Delhi Mexico City Sao Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo
    Vice President and Editorial Director, ECS: Marcia Horton Editor in Chief: Michael Hirsch Executive Editor: Tracy Dunkelberger Senior Project Manager: Carole Snyder Vice President Marketing: Patrice Jones Marketing Manager: Yez Alayan Marketing Coordinator: Kathryn Ferranti Marketing Assistant: Emma Snider Vice President and Director of Production: Vince O’Brien Managing Editor: Jeff Holcomb Senior Production Project Manager: Marilyn Lloyd Senior Operations Supervisor: Alan Fischer Operations Specialist: Lisa McDowell Art Director, Cover: Jayne Conte Text Permissions: Dana Weightman/RightsHouse, Inc. and Jen Roach/PreMediaGlobal Cover Image: © Maxppp/ZUMAPRESS.com Media Editor: Dan Sandin Composition: David Forsyth Printer/Binder: Edwards Brothers Cover Printer: Lehigh-Phoenix Color Credits and acknowledgments borrowed from other sources and reproduced, with permission, in this textbook appear on the appropriate page within text. Copyright © 2012, 2003 by Pearson Education, Inc., publishing as Prentice Hall. All rights reserved. Manufactured in the United States of America. This publication is protected by Copyright, and permission should be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. To obtain permission(s) to use material from this work, please submit a written request to Pearson Education, Inc., Permissions Department, One Lake Street, Upper Saddle River, New Jersey 07458, or you may fax your request to 201-236-3290. Many of the designations by manufacturers and sellers to distinguish their products are claimed as trade- marks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data available upon request 10 9 8 7 6 5 4 3 2 1 ISBN-13: 978-0-13-608592-8 ISBN-10: 0-13-608592-X
    To my family—DAF To my father, Jean-Jacques Ponce —JP
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    Contents I IMAGE FORMATION 1 Geometric Camera Models 1.1 1.2 Image Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Pinhole Perspective . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Weak Perspective . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 Cameras with Lenses . . . . . . . . . . . . . . . . . . . . . . . 1.1.4 The Human Eye . . . . . . . . . . . . . . . . . . . . . . . . . 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 2.2 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . Inference from Shading . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Radiometric Calibration and High Dynamic Range Images . . 2.2.2 The Shape of Specularities . . . . . . . . . . . . . . . . . . . Inferring Lightness and Illumination . . . . . . . . . . . . . . 2.2.3 . . 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 . . . . . . . . . . . . . . . . . . . . . 1 3 4 4 6 8 12 14 14 16 18 19 20 22 23 27 29 32 32 33 34 36 36 37 38 40 43 46 52 52 54 55 56 59 v
    2.5 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 vi 3 Color 3.2.1 The Color of Light Sources 3.2.2 The Color of Surfaces 3.1 Human Color Perception . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Color Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Color Receptors 3.2 The Physics of Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . 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 3.5 68 68 68 71 73 73 76 77 77 83 86 88 90 90 90 92 95 99 II EARLY VISION: JUST ONE IMAGE 105 4 Linear Filters 4.2 Shift Invariant Linear Systems 107 4.1 Linear Filters and Convolution . . . . . . . . . . . . . . . . . . . . . 107 4.1.1 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 . . . . . . . . . . . . . . . . . . . . . 112 4.2.1 Discrete Convolution . . . . . . . . . . . . . . . . . . . . . . . 113 4.2.2 Continuous Convolution . . . . . . . . . . . . . . . . . . . . . 115 4.2.3 Edge Effects in Discrete Convolutions . . . . . . . . . . . . . 118 4.3 Spatial Frequency and Fourier Transforms . . . . . . . . . . . . . . . 118 4.3.1 Fourier Transforms . . . . . . . . . . . . . . . . . . . . . . . . 119 4.4 Sampling and Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . 121 4.4.1 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 4.4.2 Aliasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Smoothing and Resampling . . . . . . . . . . . . . . . . . . . 126 4.4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 4.5.1 Convolution as a Dot Product . . . . . . . . . . . . . . . . . 131 4.5.2 Changing Basis . . . . . . . . . . . . . . . . . . . . . . . . . . 132 . . . . . . 132 4.6 Technique: Normalized Correlation and Finding Patterns 4.5 Filters as Templates
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