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Undergraduate Program

Machine Vision

Course: CSC 
Term: Spring 2013-2014

CSC 249/449 Machine Vision (Spring 2013)

Course description

Fundamentals of computer vision, including image formation, elements of human vision, low-level image processing, and pattern recognition techniques. Advanced topics include modern visual features, graphical models, model-based and data-driven approaches, and contextual inference, as well as illustrative examples of successes and challenges in applications. CSC 449, a graduate-level course, requires additional readings and assignments (including a course project).

Please check pre-requisites on the official course listing page. Some knowledge of linear algebra and basic statistics is helpful but we will review these subjects in the class (please discuss with me if you are not sure). For assignments and projects, the most common programming languages are MatlabC or C++. Note that  Matlab provides an image processing toolbox and is available on most CS department undergraduate and research networks.

Course schedule (tentative - to be updated)

weekdateschapterkey topicsCSC 249CSC 449notes
11/171, 2overview; image formation   
21/22, 1/243, 4color vision; low-level image processing   
31/29, 1/314, notes (Forsyth)texture; Intro to (review of) probability   
42/5, 2/7Duda & Hartpattern recognition concepts (incl. classifiers)   
52/12, 2/145, 6modern visual features   
62/19, 2/219, 10mid-level vision (incl. segmentation, Hough Transform)   
notes & special lecturerecognizing faces and people   
83/5, 3/7review/midterm exam    
93/12, 3/14Spring Break    
103/19, 3/21midterm solutions, 12image registrationselecting course  survey topicsselecting course project topics1-page proposal
113/26, 3/28guest lecture by Prof. Jinwei Gucomputational photography, computer graphics   
124/2, 4/411, 7, 8tracking, stereo & 3D   
134/9, 4/1118, notesrecognizing objects, scenes, actions and events   
144/16, 4/18notesdata-driven computer vision   
154/23, 4/2521, notes & special lecture content-based image annotation & retrieval (or alternatively medical image analysis)or attribute learning in vision  
164/30, 5/2notes & special lecturecontextual inference in computer vision   
175/7, 5/9final presentations  & reportsimpress us!5/75/9 
185/14, 5/16*final presentations  & reportsimpress us! 5/14, 5/16* 


Instructor and grading

Instructor: Prof. Jiebo Luo, CSB Rm 611, x65784
TA: Lam Tran
Lectures: Tuesdays and Thursdays, 12:30-13:45, CSB 601
Office hours: after classes (15:00-16:00) or by appointment (use email).

Grading (total 100%)

  1. midterm 30%
  2. homework assignments 35% (including a small project 10%)
  3. final project & presentation (for CSC 449) 30% (presentation counts 10%)
  4. final survey & presentation (for CSC 249) 30% (presentation counts 10%)
  5. class participation and effort 5%
Expectation for the final project -  something "new", e.g., 
  1. an existing algorithm applied to new data or new problems
  2. a new algorithm (or a modified version of an existing algorithm) applied to the same data
  3. new findings from a comparative study of using different algorithms for the same problem

 Textbooks and other resources

Required textbook:

Computer Vision: A Modern Approach, 2/E
David A. Forsyth and Jean Ponce

ISBN-10: 013608592X
Publisher:  Prentice Hall, 2012

Recommended textbook:
Pattern Classification, 2/E
Richard O. Duda, Peter E. Hart and David G. Stork
ISBN-10: 0471056693
Publisher: John Wiley & Sons, 2001

Recommended papers (use IEEE Xplore on the university network for free access to these and other papers):
Longuet-Higgins Prize recipients
(CVPR annual award 
for 'fundamental contributions in Computer Vision that Have Withstood the Test of Time')

  • 2005
    • Ted Adelson and John Wang, "Layered representation for motion analysis", CVPR 1993.
    • David Mumford and Jayant Shah, "Boundary detection by minimizing functionals", CVPR 1985.
  • 2006
    • H. Rowley, S. Baluja and Takeo Kanade, "Neural Network-Based Face Detection", CVPR 1996.
    • Cordelia Schmid and Roger Mohr, "Combining greyvalue invariants with local constraints for object recognition", CVPR 1996.
  • 2007
    • J. Shi and J. Malik, "Normalized Cuts and Image Segmentation", CVPR 1997.
    • E. Osuna, R. Freund, and F. Girosi, "Training Support Vector Machines: An Application to Face Detection", CVPR 1997.
  • 2008 
    • H. Schneiderman and Takeo Kanade, "Probabilistic modeling of local appearance and spatial relationships for object recognition", CVPR 1998.
    • C. Bregler and J. Malik, "Tracking people with twists and exponential maps", CVPR 1998.
  • 2009
    • Jinggang Huang, David Mumford, "Statistics of Natural Images and Models", CVPR 1999.
    • Chris Stauffer, W.E.L. Grimson, "Adaptive Background Mixture Models for Real-Time Tracking", CVPR 1999.
  • 2010
    • Pedro F. Felzenszwalb and Daniel P. Huttenlocher, "Efficient Matching of Pictorial Structures", CVPR 2000.
    • Dorin Comaniciu, Visvanathan Ramesh, and Peter Meer, "Real-Time Tracking of Non-Rigid Objects Using Mean Shift", CVPR 2000.
  • 2011
    • Paul A. Viola and Michael J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", CVPR 2001.
  • 2012  – break