This course addresses the following question: how to extract meaningful information from images and video? This is a key question in many contexts for example when we wish to equip a robot with sensing capabilities, when we wish to learn 3D models from multiple images, when we want to search for similar images in a large database or when we wish to perform the analysis of medical images to detect abnormal tissues. Some of these problems are discussed in the course e.g., image enhancement, object segmentation, object recognition, motion analysis and 3D reconstruction, The course also provides a hands on approach to computer vision by involving the students in a challenging project. Syllabus Chapter RS 1. Introduction Course presentation, geometric transformations, image formation. 2. Image processing Linear filtering, median filtering, pyramids. 3. Image restoration Aquisition model, probabilistic model, Markov random fields 4. Feature detection and matching Points, edges, lines 5. Segmentation Active contours, split and merge, graph cuts. 6. Motion analysis Image alignment, Lucas-Kanade method. 7. Recognition Face recognition instance recognition, category recognition 8. Video surveillance Motion segmentation, object tracking. 9. Structure from motion Two frame structure from motion, Factorization, bundle adjustment.