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 http://szeliski.org/Book/. 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 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 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 •