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Numbers of lectures for each topic are approximate.


Page numbers in the readings column below refer to the required textbook, E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998. Additional material will be handed out in class or posted on the table below as appropriate.

Warning: This page is under construction. Materials are missing in particular for items in red. Active links in the Module column point to lecture notes.

Module Description Required Readings Optional Readings Software and Data
Introduction purpose, state of the art pp. 1-13    
Image Formation projection, sensing, color pp. 15-40, Cameras, Litwiller, Bayer Kolb, Maeda  
Image Processing filtering (low-pass and median), derivatives, and edges pp. 51-82, Filtering, Weiss   Matlab smoothing and gradient code
Geometric Calibration interior and exterior calibration, rectification pp. 123-138, 143-145, 155-161   Matlab camera calibration package (Bouguet)
Math Methods linear algebra, vectors, rotations Lecture notes on geometric calibration and math methods have been split into two separate sets for greater portability.
Stereo epipolar geometry, correspondence, triangulation pp. 139-143, 150-155, 161-175, Stereo, Stereo 2   The Middlebury stereo web page
Motion + class handouts detection and tracking of point features, optical flow pp. 82-85, 177-199   Matlab code to experiment with SSD tracking
Object Tracking Kalman filter, condensation, tracking humans pp. 199-203, Condensation   Matlab code for the Kalman filter. The Condensation web page.
Structure from Motion multiframe reconstruction under affine and perspective projection geometry pp. 203-212, Factorization Multibody factorization Video demonstrations of the factorization method
Texture texture descriptors and classification pp. 235-237    
2D Shape splines, snakes, PCA descriptors pp. 95-121, 262-270    
Project Descriptions 5-minute student presentations      
3D Shape parts, skeletons, surface models, aspect graphs      
Recognition character classification, pedestrian and face recognition/detection pp. 247-249