Computer vision : algorithms and applications / Richard Szeliski.

Includes bibliographical references (p. [691]-792) and index.

Introduction. What is computer vision?; A brief history; Book overview; Sample syllabus; Notation -- Image formation. Geometric primitives and transformations; Photometric image formation; The digital camera -- Image processing. Point operators; Linear filtering; More neighborhood operators; Fourier transforms; Pyramids and wavelets; Geometric transformations; Global optimization -- Feature detection and matching. Points and patches; Edges; Lines -- Segmentation. Active contours; Split and merge; Mean shift and mode finding; Normalized cuts; Graph cuts and energy-based methods -- Feature-based alignment. 2D and 3D feature-based alignment; Pose estimation; Geometric intrinsic calibration -- Structure from motion. Triangulation; Two-frame structure from motion; Factorization; Bundle adjustment; Constrained structure and motion -- Dense motion estimation. Translational alignment; Parametric motion; Spline-based motion; Optical flow; Layered motion -- Image stitching. Motion models; Global alignment; Compositing -- Computational photography. Photometric calibration; High dynamic range imaging; Super-resolution and blur removal; Image matting and compositing; Texture analysis and synthesis -- Stereo correspondence. Epipolar geometry; Sparse correspondence; Dense correspondence; Local methods; Global optimization; Multi-view stereo -- 3D reconstruction. Shape from X; Active rangefinding; Surface representations; Point-base representations; Volumetric representations; Model-based reconstruction; Recovering texture maps and albedos -- Image-based rendering. View interpolation; Layered depth images; Light fields and Lumigraphs; Environment mattes; Video-base rendering -- Recognition. Object detection; Face recognition; Instance recognition; Category recognition; Context and scene understanding; Recognition databases and test sets.

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