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.