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Homeworks, handouts, lecture notes
This course will cover basis principles of image formation, different algorithms for estimating various quantities from single or multiple images (video). Apllications to vision-based control, 3D reconstruction, video analysis, surveillance and object recognition will be discussed.
1. Representation of 3-D moving scene : rigid body motion, Euclidean, affine and projective transformations.
2. Image formation: geometric and photometric aspects of image formation process, grey level and color images
3. Image features and Correspondence: geometric and photometric features, feature detection and matching, optical flow
4. Stereo - Two view geometry: camera pose and 3D structure recovery from two views, camera calibration, 3-D reconstruction
6. Multiview Geometry: recovery of camera poses and 3D structure from multiple views, recursive estimation from motion sequences
8. Grouping and Segmentation: detection and recovery of multiple motions
9. Detection and Recognition of 0bjects in Images: object representations and classification methods
10. Selected topics: vision based control, image based rendering pipeline, vision for human computer intraction, recognition
Grading: Homeworks (about every 2 weeks)
40% Midterm: 30% Final project: 30%
Prerequisites:linear algebra, calculus
Lecture Materials: Lecture notes and slides