MIT Department of Electrical Engineering & Computer Science

EECS Spring 1997 Catalogue Supplement

6.965 Three Dimensional Computer Vision (G)

WF 11-12:30, 37-232
FIRST MEETING: WEDNESDAY, 5 MARCH
Prof. Olivier Faugeras, olivier.faugeras@sophia.inria.fr
3-0-3
Prerequisites: basic notions of linear algebra, calculus, probabilities, random processes, image and signal processing.

The course covers intermediate and advanced topics in three-dimensional Computer Vision. It is intended to provide the theoretical tools that are necessary to tackle important applications of Computer Vision such as the interactive and automatic modelling of three-dimensional objects and scenes, the navigation of robots, the synthesis and coding of images. Twelve lectures will be given over six weeks. They will be partially based upon the book by the author entitled "Three-dimensional Computer Vision: a geometric viewpoint", MIT Press 1993.

I) Introduction, motivation from applications - Projective geometry.
II) More projective geometry and some Grassmann-Cayley algebra (I).
III) More Grassmann-Cayley algebra (II).
IV) Modelling cameras - Perspective, paraperspective, affine models - nonlinear distortions.
V) Traditional camera calibration - when does it fail?
VI) Systems of two cameras: epipolar geometry, Fundamental matrixes, the role of planes - Stereo - Applications: obstacle detection, image mosaics.
VII) Representation and computation of uncertainty.
VIII) Practical estimation of the Fundamental matrix - Outlier rejection.
IX) Relation with the Essential matrix - motion estimation.
X) A bit more Grasmann-Cayley algebra - Systems of three and more cameras - trilinear relations - Practical estimation techniques - Application: new image synthesis from images.
XI) Self-calibration of a camera.
XII) Complete self-calibration of systems of many cameras - Notions of photogrammetry - Multicamera stereo - Application to scene modelling.