This course will cover basis principles of image formation, basic image processing algorithms and 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 scenes : rigid body motion, euclidean, affine and projective transformations.
    2. Image formation: geometric and photometric aspects of image formation process, binary, 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. Image Matching and Tracking : matching of multiple views, tracking and video analysis
    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 slides, lecture notes

Recommended Textbook:

Other 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 - the majority of homeworks will require using Matlab. You can buy a student version in Johnson center
or use the ITE labs, which have it installed.