Duke Vision Laboratory

CPS 296.1
Introduction to Computer Vision
Spring 2006

Course Mechanics

Project Guidelines

Project Information

General Resources

 

 


Places and Dates

Announcements

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Newsgroup

A newsgroup is available for this class. The group can be accessed as follows:

 Course Outline, Readings, and Software

This course explores some of the most successful ideas in computer vision at an introductory level. Linear algebra and probability are prerequisites. For each of the following topics, we will cover the basic concepts, look at one or two algorithms that work well, and discuss limitations and possible extensions. The numbers of lectures indicated for each topic are approximate.
 

Lectures

Module

Description

Readings

Samples and Software

1

Introduction

State of the art, fundamental challenges, IGVC competition

 

Powerpoint with examples

2

IGVC

Strategy. Project assignments. Hardware, software, logistics.

IGVC rules, DARPA tech papers

 

3, 4

Image Analysis

Convolution, smoothing, derivatives, median filtering, edge detection

[1], [2]

Convolution code, Edge samples

5

IGVC

IGVC status, issues, IGVC/DARPA tech papers

 

 

6

Image Formation

Relationship between world and image

[3]

 

7, 8, 9, 10

Stereo

Geometry, similarity metrics, rank and census transforms.
Dynamic-programming, belief propagation

[4], [5], [6], [7], [8]

Stereo block matching browser.

11 IGVC IGVC. Shashi and Stephen on Stanley, Whitespace    

12, 13

Optical Flow

Definitions, issues, algorithms, with emphasis on Lucas and Kanade

[9], [10]

SSD motion software

14 IGVC IGVC. Seda, Amber, Abishek on Prospect 11, planet traversability, collision avoidance, stereo obstacle avoidance    

15, 16

Segmentation

For both images and flow. Split/merge methods, clustering

[11], [12], [13]

k-means and EM software

17 IGVC IGVC. Sam, Christopher, Laura on Calculon, probabilistic algorithms for robotics, and Ion.    

Spring Recess

18, 19, 20

Tracking

Appearance and motion models, Kalman filtering, particle filters

[14], [15], [16]

Kalman code for [14]

21 IGVC IGVC. Monika and Joe on Blue Team and Team Caltech    

22, 23, 24

Recognition

Generative models, discriminative classifiers. Features. Sample algorithms

[17], [18], [19]

k nearest neighbors on the plane. See also the OpenCV library

25, 26

IGVC

IGVC final project presentations

 

 

  1. C. Tomasi. Convolution, smooting, and image derivatives. Class handout in PDF.
  2. J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986. Class handout in paper form.
  3. C. Tomasi. Image Formation. Class handout in PDF.
  4. M. Z. Brown, D. Burschka, and G. D. Hager. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (8) 9931008, 2003. PDF.
  5. R. Zabih and J. Woodfill. Non-parametric local transforms for computing visual correspondence. European Conference on Computer Vision, II:151158, 2004. PDF.
  6. J. I. Woodfill, G. Gordon, R. Buck. Tyzx DeepSea high speed stereo vision system. IEEE Conference on Computer Vision and Pattern Recognition Workshop, 3:4145, 2004. PDF.
  7. A. F. Bobick and S. S. Intille. Large occlusion stereo. International Journal of Computer Vision, 33(3):181-200, 1999. PDF.
  8. J. Sun, N. Zheng and H. Shum. Stereo Matching Using Belief Propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (7), 114, 2003. PDF.
  9. J. L. Barron, D. J. Fleet and S. S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 12(1), 4377, 1994. The Postscript version is more legible on screen than the PDF version, but both print well.
  10. C. Tomasi and T. Kanade.  Detection and tracking of point features. Technical Report CMU-CS-91-132, 1991. PDF.
  11. C. Tomasi. Estimating Gaussian mixture densities with EM - A Tutorial. Class handout in PDF. See also tutorials on EM by Dellaert, Minka, Rennie, and Weiss.
  12. S. L. Horowitz and T. Pavlidis. Picture segmentation by a tree traversal algorithm. Journal of the ACM 23(2):368388, 1976. PDF.
  13. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888905, 2000. PDF.
  14. C. Tomasi. Stochastic Estimation. Chapter 7 of the notes for mathematical methods class. PDF.
  15. M. Isard and A. Blake CONDENSATION - Conditional density estimation for visual tracking. International Journal of Computer Vision, 29(1), 528, 1998. PDF.
  16. A. Litvin, J. Konrad, W. C. Karl. Probabilistic video stabilization using Kalman filtering and mosaicking.IS&T/SPIE Symposium on Electronic Imaging, Image and Video Communications and Processing, 2003. PDF and videos.
  17. Y. Freund and R. E. Schapire. Experiments with a New Boosting Algorithm. 13th International Conference on Machine Learning, 148156, 1996. PDF.
  18. P. Viola, M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2), 137154, 2004. PDF.
  19. M. J. Jones and J. M. Rehg. Statistical color models with application to skin detection. International Journal of Computer Vision, 46(1):8196, 2002. PDF.

Homework

Teaching Staff

Carlo Tomasi, Instructor

E-mail address: tomasi@cs.duke.edu
Office Hours: By appointment
Office Location: D213 LSRC
Office Phone: (919) 660-6539
FAX: (919) 660-6519