Richard Szeliski - Computer Vision. Algorithms and Applications.pdf

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Computer Vision:
Algorithms and Applications
Richard Szeliski
September 3, 2010 draft
c 2010 Springer
This electronic draft is for non-commercial personal use only,
and may not be posted or re-distributed in any form.
Please refer interested readers to the book’s Web site at
This book is dedicated to my parents,
Zdzisław and Jadwiga,
and my family,
Lyn, Anne, and Stephen.
1
Introduction
1
What is computer vision? A brief history
Book overview Sample syllabus Notation
2
Image formation
29
^
Geometric primitives and transformations
Photometric image formation
The digital camera
3
Image processing
99
Point operators
Linear filtering
Pyramids and wavelets Geometric transformations
Global optimization
More neighborhood operators
Fourier transforms
4
Feature detection and matching
205
Points and patches
Edges
Lines
5
Segmentation
267
Active contours Split and merge
Mean shift and mode finding Normalized cuts
Graph cuts and energy-based methods
6
Feature-based alignment
309
2D and 3D feature-based alignment
Pose estimation
Geometric intrinsic calibration
7
Structure from motion
343
Triangulation Two-frame structure from motion
Factorization Bundle adjustment
Constrained structure and motion
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8
Dense motion estimation
381
Translational alignment Parametric motion
Spline-based motion Optical flow
Layered motion
9
Image stitching
427
Motion models Global alignment
Compositing
10
Computational photography
467
Photometric calibration
High dynamic range imaging
Super-resolution and blur removal
Image matting and compositing
Texture analysis and synthesis
11
Stereo correspondence
533
Epipolar geometry Sparse correspondence
Dense correspondence Local methods
Global optimization Multi-view stereo
12
3D reconstruction
577
Shape from X Active rangefinding
Surface representations Point-based representations
Volumetric representations Model-based reconstruction
Recovering texture maps and albedos
13
Image-based rendering
619
View interpolation Layered depth images
Light fields and Lumigraphs Environment mattes
Video-based rendering
14
Recognition
655
Object detection Face recognition
Instance recognition Category recognition
Context and scene understanding
Recognition databases and test sets
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