

Syllabus
Page numbers in the readings column below refer to the required textbook, E. Trucco and A. Verri, Introductory Techniques for 3D 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. 113 


Image Formation 
projection, sensing, color 
pp. 1540, Cameras, Litwiller, Bayer 
Kolb, Maeda 

Image Processing 
filtering (lowpass and median), derivatives, and edges 
pp. 5182, Filtering, Weiss 

Matlab smoothing and gradient code 
Geometric Calibration 
interior and exterior calibration, rectification 
pp. 123138, 143145, 155161 

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. 139143, 150155, 161175, Stereo, Stereo 2 

The Middlebury stereo web page 
Motion + class handouts 
detection and tracking of point features, optical flow 
pp. 8285, 177199 

Matlab code to experiment with SSD tracking 
Object Tracking 
Kalman filter, condensation, tracking humans 
pp. 199203, Condensation 

Matlab code for the Kalman filter. The Condensation web page. 
Structure from Motion 
multiframe reconstruction under affine and perspective projection geometry 
pp. 203212, Factorization 
Multibody factorization 
Video demonstrations of the factorization method 
Texture 
texture descriptors and classification 
pp. 235237 


2D Shape 
splines, snakes, PCA descriptors 
pp. 95121, 262270 


Project Descriptions 
5minute student presentations 



3D Shape 
parts, skeletons, surface models, aspect graphs 



Recognition 
character classification, pedestrian and face recognition/detection 
pp. 247249 


 Cameras: Wikipedia entry on cameras.
 Kolb: C. Kolb, D. Mitchell, and P. Hanrahan, 1995. A realistic camera model for computer graphics. In Proceedings of the 22nd Annual Conference on Computer Graphics and interactive Techniques S. G. Mair and R. Cook, Eds. SIGGRAPH '95. ACM Press, New York, NY, 317324.
 Maeda: P. Y. Maeda, P. B. Catrysse, and B. A. Wandel, 2005. Integrating lens design with digital camera simulation. In Proceedings of SPIE  Volume 5678, Digital Photography, N. Sampat, J. M. DiCarlo, R. J. Motta, Editors, 4858.
 Litwiller: D. Litwiller, 2001. CCD vs. CMOS: facts and fiction. In Photonics Spectra.
 Bayer: Wikipedia entry on the Bayer pattern. Also look at the Reference and External Links.
 Filtering: C. Tomasi, 2005. Convolution, smooting, and image derivatives.
 Weiss: B. Weiss, 2006. Fast median and bilateral filtering. In ACM SIGGRAPH 2006 Papers. ACM Press, New York, NY, 519526.
 Stereo: D. Scharstein and R. Szeliski, 2002. A Taxonomy and Evaluation of Dense TwoFrame Stereo Correspondence Algorithms. International Journal of Computer Vision, 47(13), 742.
 Stereo 2: M. Z. Brown, D. Burschka, and G. D. Hager, 2003. Advances in Computational Stereo. IEEE Transactions on Pattern Analysis and Machine Inltelligence, 25(8), 9931008.
 Condensation: M. Isard and A. Blake, 1998. CONDENSATIONâ€”conditional density propagation for visual tracking, International Journal on Computer Vision, 29(1), 528.
 Factorization: C.Tomasi and T. Kanade, 1992. Shape and Motion from Image Streams under Orthography: a
Factorization Method. International Journal on Computer Vision, 9(2), 137154.

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