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Computer Vision: Algorithms and Applications 2011.pdf

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    Cover
    Computer Vision: Algorithms and Applications
    Copyright
    9781848829343
    Preface
    Acknowledgements
    Contents
    Chapter 1 Introduction
    1.1 What is computer vision?
    1.2 A brief history
    1.3 Book overview
    1.4 Sample syllabus
    1.5 A note on notation
    1.6 Additional reading
    Chapter 2 Image formation
    2.1 Geometric primitives and transformations
    2.1.1 Geometric primitives
    2.1.2 2D transformations
    2.1.3 3D transformations
    2.1.4 3D rotations
    Euler angles
    Axis/angle (exponential twist)
    Unit quaternions
    Which rotation representation is better?
    2.1.5 3D to 2D projections
    Orthography and para-perspective
    Perspective
    Camera intrinsics
    A note on focal lengths
    Camera matrix
    Plane plus parallax (projective depth)
    Mapping from one camera to another
    Object-centered projection
    2.1.6 Lens distortions
    2.2 Photometric image formation
    2.2.1 Lighting
    2.2.2 Reflectance and shading
    The Bidirectional Reflectance Distribution Function (BRDF)
    Diffuse reflection
    Specular reflection
    Phong shading
    Di-chromatic reflection model
    Global illumination (ray tracing and radiosity)
    2.2.3 Optics
    Chromatic aberration
    Vignetting
    2.3 The digital camera
    2.3.1 Sampling and aliasing
    2.3.2 Color
    CIE RGB and XYZ
    L*a*b* color space
    Color cameras
    Color filter arrays
    Color balance
    Gamma
    Other color spaces
    2.3.3 Compression
    2.4 Additional reading
    2.5 Exercises
    Chapter 3 Image processing
    3.1 Point operators
    3.1.1 Pixel transforms
    3.1.2 Color transforms
    3.1.3 Compositing and matting
    3.1.4 Histogram equalization
    Locally adaptive histogram equalization
    3.1.5 Application: Tonal adjustment
    3.2 Linear filtering
    Padding (border effects)
    3.2.1 Separable filtering
    3.2.2 Examples of linear filtering
    3.2.3 Band-pass and steerable filters
    Summed area table (integral image)
    Recursive filtering
    3.3 More neighborhood operators
    3.3.1 Non-linear filtering
    Median filtering
    Bilateral filtering
    Iterated adaptive smoothing and anisotropic diffusion
    3.3.2 Morphology
    3.3.3 Distance transforms
    3.3.4 Connected components
    3.4 Fourier transforms
    3.4.1 Fourier transform pairs
    3.4.2 Two-dimensional Fourier transforms
    3.4.3 Wiener filtering
    Discrete cosine transform
    3.4.4 Application: Sharpening, blur, and noise removal
    3.5 Pyramids and wavelets
    3.5.1 Interpolation
    3.5.2 Decimation
    3.5.3 Multi-resolution representations
    3.5.4 Wavelets
    3.5.5 Application: Image blending
    3.6 Geometric transformations
    3.6.1 Parametric transformations
    MIP-mapping
    Elliptical Weighted Average
    Anisotropic filtering
    Multi-pass transforms
    3.6.2 Mesh-based warping
    3.6.3 Application: Feature-based morphing
    3.7 Global optimization
    3.7.1 Regularization
    3.7.2 Markov random fields
    3.7.3 Application: Image restoration
    3.8 Additional reading
    3.9 Exercises
    Chapter 4 Feature detection and matching
    4.1 Points and patches
    4.1.1 Feature detectors
    Scale invariance
    Rotational invariance and orientation estimation
    Affine invariance
    4.1.2 Feature descriptors
    4.1.3 Feature matching
    Matching strategy and error rates
    Efficient matching
    Feature match verification and densification
    4.1.4 Feature tracking
    4.1.5 Application: Performance-driven animation
    4.2 Edges
    4.2.1 Edge detection
    Scale selection and blur estimation
    Color edge detection
    Combining edge feature cues
    4.2.2 Edge linking
    4.2.3 Application: Edge editing and enhancement
    4.3 Lines
    4.3.1 Successive approximation
    4.3.2 Hough transforms
    4.3.3 Vanishing points
    4.3.4 Application: Rectangle detection
    4.4 Additional reading
    4.5 Exercises
    Chapter 5 Segmentation
    5.1 Active contours
    5.1.1 Snakes
    Elastic nets and slippery springs
    Splines and shape priors
    5.1.2 Dynamic snakes and CONDENSATION
    5.1.3 Scissors
    5.1.4 Level Sets
    5.1.5 Application: Contour tracking and rotoscoping
    5.2 Split and merge
    5.2.1 Watershed
    5.2.2 Region splitting (divisive clustering)
    5.2.3 Region merging (agglomerative clustering)
    5.2.4 Graph-based segmentation
    5.2.5 Probabilistic aggregation
    5.3 Mean shift and mode finding
    5.3.1 K-means and mixtures of Gaussians
    5.3.2 Mean shift
    5.4 Normalized cuts
    5.5 Graph cuts and energy-based methods
    5.5.1 Application: Medical image segmentation
    5.6 Additional reading
    5.7 Exercises
    Chapter 6 Feature-based alignment
    6.1 2D and 3D feature-based alignment
    6.1.1 2D alignment using least squares
    6.1.2 Application: Panography
    6.1.3 Iterative algorithms
    6.1.4 Robust least squares and RANSAC
    Uncertainty modeling
    6.1.5 3D alignment
    6.2 Pose estimation
    6.2.1 Linear algorithms
    6.2.2 Iterative algorithms
    6.2.3 Application: Augmented reality
    6.3 Geometric intrinsic calibration
    6.3.1 Calibration patterns
    Planar calibration patterns
    6.3.2 Vanishing points
    6.3.3 Application: Single view metrology
    6.3.4 Rotational motion
    6.3.5 Radial distortion
    6.4 Additional reading
    6.5 Exercises
    Chapter 7 Structure from motion
    7.1 Triangulation
    7.2 Two-frame structure from motion
    Pure translation (known rotation)
    Pure rotation
    7.2.1 Projective (uncalibrated) reconstruction
    7.2.2 Self-calibration
    7.2.3 Application: View morphing
    7.3 Factorization
    7.3.1 Perspective and projective factorization
    7.3.2 Application: Sparse 3D model extraction
    7.4 Bundle adjustment
    7.4.1 Exploiting sparsity
    7.4.2 Application: Match move and augmented reality
    7.4.3 Uncertainty and ambiguities
    7.4.4 Application: Reconstruction from Internet photos
    7.5 Constrained structure and motion
    7.5.1 Line-based techniques
    7.5.2 Plane-based techniques
    7.6 Additional reading
    7.7 Exercises
    Chapter 8 Dense motion estimation
    8.1 Translational alignment
    8.1.1 Hierarchical motion estimation
    8.1.2 Fourier-based alignment
    8.1.3 Incremental refinement
    8.2 Parametric motion
    8.2.1 Application: Video stabilization
    8.2.2 Learned motion models
    8.3 Spline-based motion
    8.3.1 Application: Medical image registration
    8.4 Optical flow
    8.4.1 Multi-frame motion estimation
    8.4.2 Application: Video denoising
    8.4.3 Application: De-interlacing
    8.5 Layered motion
    8.5.1 Application: Frame interpolation
    8.5.2 Transparent layers and reflections
    8.6 Additional reading
    8.7 Exercises
    Chapter 9 Image stitching
    9.1 Motion models
    9.1.1 Planar perspective motion
    9.1.2 Application: Whiteboard and document scanning
    9.1.3 Rotational panoramas
    9.1.4 Gap closing
    9.1.5 Application: Video summarization and compression
    9.1.6 Cylindrical and spherical coordinates
    9.2 Global alignment
    9.2.1 Bundle adjustment
    9.2.2 Parallax removal
    9.2.3 Recognizing panoramas
    9.2.4 Direct vs. feature-based alignment
    9.3 Compositing
    9.3.1 Choosing a compositing surface
    9.3.2 Pixel selection and weighting (de-ghosting)
    9.3.3 Application: Photomontage
    9.3.4 Blending
    9.4 Additional reading
    9.5 Exercises
    Chapter 10 Computational photography
    10.1 Photometric calibration
    10.1.1 Radiometric response function
    10.1.2 Noise level estimation
    10.1.3 Vignetting
    10.1.4 Optical blur (spatial response) estimation
    10.2 High dynamic range imaging
    10.2.1 Tone mapping
    10.2.2 Application: Flash photography
    10.3 Super-resolution and blur removal
    10.3.1 Color image demosaicing
    10.3.2 Application: Colorization
    10.4 Image matting and compositing
    10.4.1 Blue screen matting
    10.4.2 Natural image matting
    10.4.3 Optimization-based matting
    10.4.4 Smoke, shadow, and flash matting
    10.4.5 Video matting
    10.5 Texture analysis and synthesis
    10.5.1 Application: Hole filling and inpainting
    10.5.2 Application: Non-photorealistic rendering
    10.6 Additional reading
    10.7 Exercises
    Chapter 11 Stereo correspondence
    11.1 Epipolar geometry
    11.1.1 Rectification
    11.1.2 Plane sweep
    11.2 Sparse correspondence
    11.2.1 3D curves and profiles
    11.3 Dense correspondence
    11.3.1 Similarity measures
    11.4 Local methods
    11.4.1 Sub-pixel estimation and uncertainty
    11.4.2 Application: Stereo-based head tracking
    11.5 Global optimization
    11.5.1 Dynamic programming
    11.5.2 Segmentation-based techniques
    11.5.3 Application: Z-keying and background replacement
    11.6 Multi-view stereo
    11.6.1 Volumetric and 3D surface reconstruction
    11.6.2 Shape from silhouettes
    11.7 Additional reading
    11.8 Exercises
    Chapter 12 3D reconstruction
    12.1 Shape from X
    12.1.1 Shape from shading and photometric stereo
    12.1.2 Shape from texture
    12.1.3 Shape from focus
    12.2 Active rangefinding
    12.2.1 Range data merging
    12.2.2 Application: Digital heritage
    12.3 Surface representations
    12.3.1 Surface interpolation
    12.3.2 Surface simplification
    12.3.3 Geometry images
    12.4 Point-based representations
    12.5 Volumetric representations
    12.5.1 Implicit surfaces and level sets
    12.6 Model-based reconstruction
    12.6.1 Architecture
    12.6.2 Heads and faces
    12.6.3 Application: Facial animation
    12.6.4 Whole body modeling and tracking
    12.7 Recovering texture maps and albedos
    12.7.1 Estimating BRDFs
    12.7.2 Application: 3D photography
    12.8 Additional reading
    12.9 Exercises
    Chapter 13 Image-based rendering
    13.1 View interpolation
    13.1.1 View-dependent texture maps
    13.1.2 Application: Photo Tourism
    13.2 Layered depth images
    13.2.1 Impostors, sprites, and layers
    13.3 Light fields and Lumigraphs
    13.3.1 Unstructured Lumigraph
    13.3.2 Surface light fields
    13.3.3 Application: Concentric mosaics
    13.4 Environment mattes
    13.4.1 Higher-dimensional light fields
    13.4.2 The modeling to rendering continuum
    13.5 Video-based rendering
    13.5.1 Video-based animation
    13.5.2 Video textures
    13.5.3 Application: Animating pictures
    13.5.4 3D Video
    13.5.5 Application: Video-based walkthroughs
    13.6 Additional reading
    13.7 Exercises
    Chapter 14 Recognition
    14.1 Object detection
    14.1.1 Face detection
    14.1.2 Pedestrian detection
    14.2 Face recognition
    14.2.1 Eigenfaces
    14.2.2 Active appearance and 3D shape models
    14.2.3 Application: Personal photo collections
    14.3 Instance recognition
    14.3.1 Geometric alignment
    14.3.2 Large databases
    14.3.3 Application: Location recognition
    14.4 Category recognition
    14.4.1 Bag of words
    14.4.2 Part-based models
    14.4.3 Recognition with segmentation
    14.4.4 Application: Intelligent photo editing
    14.5 Context and scene understanding
    14.5.1 Learning and large image collections
    14.5.2 Application: Image search
    14.6 Recognition databases and test sets
    14.7 Additional reading
    14.8 Exercises
    Chapter 15 Conclusion
    Appendix A Linear algebra and numerical techniques
    A.1 Matrix decompositions
    A.1.1 Singular value decomposition
    A.1.2 Eigenvalue decomposition
    A.1.3 QR factorization
    A.1.4 Cholesky factorization
    A.2 Linear least squares
    A.2.1 Total least squares
    A.3 Non-linear least squares
    A.4 Direct sparse matrix techniques
    A.4.1 Variable reordering
    A.5 Iterative techniques
    A.5.1 Conjugate gradient
    A.5.2 Preconditioning
    A.5.3 Multigrid
    Appendix B Bayesian modeling and inference
    B.1 Estimation theory
    B.1.1 Likelihood for multivariate Gaussian noise
    B.2 Maximum likelihood estimation and least squares
    B.3 Robust statistics
    B.4 Prior models and Bayesian inference
    B.5 Markov random fields
    B.5.1 Gradient descent and simulated annealing
    B.5.2 Dynamic programming
    B.5.3 Belief propagation
    B.5.4 Graph cuts
    B.5.5 Linear programming
    B.6 Uncertainty estimation (error analysis)
    Appendix C Supplementary material
    C.1 Data sets
    C.2 Software
    C.3 Slides and lectures
    C.4 Bibliography
    References
    Index
    Texts in Computer Science Editors David Gries Fred B. Schneider For further volumes: www.springer.com/series/3191
    Richard Szeliski Computer Vision Algorithms and Applications 123
    Dr. Richard Szeliski Microsoft Research One Microsoft Way 98052-6399 Redmond Washington USA szeliski@microsoft.com Series Editors David Gries Department of Computer Science Upson Hall Cornell University Ithaca, NY 14853-7501, USA Fred B. Schneider Department of Computer Science Upson Hall Cornell University Ithaca, NY 14853-7501, USA ISSN 1868-0941 ISBN 978-1-84882-934-3 DOI 10.1007/978-1-84882-935-0 Springer London Dordrecht Heidelberg New York e-ISSN 1868-095X e-ISBN 978-1-84882-935-0 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2010936817 © Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
    This book is dedicated to my parents, Zdzisław and Jadwiga, and my family, Lyn, Anne, and Stephen.
    1 Introduction What is computer vision? • A brief history • Book overview • Sample syllabus • Notation n^ 2 Image formation Geometric primitives and transformations • Photometric image formation • The digital camera 3 Image processing Point operators • Linear filtering • More neighborhood operators • Fourier transforms • Pyramids and wavelets • Geometric transformations • Global optimization 4 Feature detection and matching Points and patches • Edges • Lines 5 Segmentation Active contours • Split and merge • Mean shift and mode finding • Normalized cuts • Graph cuts and energy-based methods 6 Feature-based alignment 2D and 3D feature-based alignment • Pose estimation • Geometric intrinsic calibration 7 Structure from motion Triangulation • Two-frame structure from motion • Factorization • Bundle adjustment • Constrained structure and motion 1 27 87 181 235 273 303
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