COS 496 - Computer Vision

Outline

Spring, 2002

Course home      |      Outline      |      Assignments

Readings

The textbook for the course is Computer Vision - A Modern Approach by David Forsyth and Jean Ponce. Since the book didn't quite make it into print in time, a draft of the book is available here. Since the book is rather large (50 MB), we suggest you download it once to your own computer (right-click on the link and select "Save").

In addition, we will be reading a few recent vision papers. These will be provided in paper and/or electronic form.

The book and most other reading materials are in PDF format. If you don't have Acrobat Reader, you can get it from Adobe.


Topics

Date Topic Readings Notes
Mon, Feb 4 Introduction to human and computer vision Ch. 9.2, 15.1 Lecture notes
Wed, Feb 6 Image formation: optics and imagers Ch. 5.1-5.2 Lecture notes
Mon, Feb 11 Filtering, edge detection Ch. 7-8 Lecture notes
Wed, Feb 13 Feature detection, Hough transform Ch. 16.1-16.2 Lecture notes
Mon, Feb 18 Deformable contours Snakes paper Lecture notes
Wed, Feb 20 Intro to motion; optical flow Lucas-Kanade paper Lecture notes
Mon, Feb 25 Tracking; Motion models; Kalman filtering Ch. 18.1-18.2 Lecture notes;
SIGGRAPH course notes
Wed, Feb 27 Image alignment and mosaicing   Lecture notes
Mon, Mar 4 3D vision Ch. 12.1 Lecture notes
Wed, Mar 6 Camera geometry and calibration Ch. 5, 11.1 Lecture notes
Mon, Mar 11 Stereo Ch. 12.1-12.2 Lecture notes
Wed, Mar 13 More on stereo:
diffusion and energy minimization methods 		Non-linear diffusion paper;
Graph cuts paper 		Lecture notes
Mon, Mar 18 No class - spring break    
Wed, Mar 20 No class - spring break    
Mon, Mar 25 Multibaseline stereo; voxel coloring Ch. 12.3; Voxel coloring paper Lecture notes
Wed, Mar 27 Affine structure from motion Ch. 13.3-13.4 Lecture notes
Mon, Apr 1 More on structure from motion Ch. 14  
Wed, Apr 3 Shape from texture; shape from shading Ch. 2, 10; Photometric stereo paper Lecture notes
Mon, Apr 8 Segmentation and clustering Ch. 15 Lecture notes
Wed, Apr 10 Probabilistic and statistical techniques;
Bayes's rule; expectation maximization 		Ch. 6, 17 Lecture notes
Mon, Apr 15 Recognition, PCA, templates Ch. 20-21 Lecture notes
Wed, Apr 17 Recognition and matching in 3D Spin images paper;
Shape distributions paper 		Lecture notes
Mon, Apr 22 3D scanning: theory and case studies Ch. 23; Digital Michelangelo paper;
Pietà paper; Great Buddha paper 		Lecture notes
Wed, Apr 24 Measuring the plenoptic function;
image-based modeling and rendering 		Ch. 25; Light field paper;
Lumigraph paper 		Lecture notes
Mon, Apr 29 Light and Color Ch. 3 Lecture notes
Wed, May 1 Final project presentations    

Last update 12:05:14 29-Dec-2010
cs496@princeton.edu