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. 

Syllabus:
    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 
Recommended Textbooks:
Invitation to 3D Vision:  From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S. Sastry (for part I of the course)
Introductory Techniques for 3D computer Vision. E. Trucco and A. Verri, Prentice-Hall, 1998
Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003

Required Software:
Matlab