This book provides an introduction to computer vision. Topics include: basic notation and mathematical concepts for describing an image, and key concepts for mapping an image into an image; topologic and geometric basics for analysing image regions and distributions of image values, and identification patterns; optic flow for representing dense motion, definition, and feature tracking using the Kalman filter; approaches for image binarization and segmentation of still images or video frames; components of a computer vision system (camera geometry and photometry, coordinate systems, and camera calibration); techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding; stereo matchers, and the phase-congruency model for image features; description of AdaBoost and use of random forests; classroom-tested programming exercises and review questions. -- Edited summary from book.