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CS 682
Computer Vision
Time/Location: Wednesday 4:30-7:10, ENT 176
Instructor: Dr. Jana Kosecka
ST2, 417, kosecka@cs.gmu.edu

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 
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: