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computer vision algorithm and applications
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更新于2023-05-31
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computer vision algorithm and applications
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This book is dedicated to my parents,
Zdzisław and Jadwiga,
and my family,
Lyn, Anne, and Stephen.
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1 Introduction 1
What is computer vision? • A brief history •
Book overview • Sample syllabus • Notation
n
^
2 Image formation 29
Geometric primitives and transformations •
Photometric image formation •
The digital camera
3 Image processing 99
Point operators • Linear filtering •
More neighborhood operators • Fourier transforms •
Pyramids and wavelets • Geometric transformations •
Global optimization
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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