‎"Behind every stack of books there is a flood of knowledge."

Advances in Computer Vision – Spring 2010



Advanced topics in computer vision with a focus on the use of machine learning techniques and applications in graphics and human-computer interface. Topics include image representations, texture models, structure-from-motion algorithms, Bayesian techniques, object and scene recognition, tracking, shape modeling, and image databases. Applications may include face recognition, multimodal interaction, interactive systems, cinematic special effects, and photorealistic rendering. Covers topics complementary to 6.801/6.866; these subjects may be taken in sequence.

General information

Lecture: Mondays/Wednesdays 1:00-2:30pm
Room: 2-139 (where is this?)

InstructorAntonio Torralba
E -mail: trrlb@mt.d (fill the missing vowels)
Office: D432

T.A.Joseph Lim (office hours: Wednesday 3:30 – 4:30, office D428)


  • Textbook: new book by Rick Szeliski (not published yet, but a draft is available online)
  • Textbook: Computer vision: a modern approach, by Forsyth and Ponce. Prentice Hall, 2002.
  • The class will make use of MATLAB.


  • problem sets: 1/3
  • 2 take-home exams: 1/3
  • final project: 1/3 

– Visit the course evaluation website (available until May 15).

– Gallery of pinhole and anaglyph images here.


Date Topic Slides Readings Assignments Aditional material
Feb 3 Course introduction lecture1.ppt (.pdf) Build your own pinhole camera
Flickr Digital Pinhole group
Problem set 1.pdf 
Anaglyph pinhole camera

(due on Feb 17)

Feb 8 The structure of images lecture2.ppt (.pdf) Rick’s book, sections 3.2, 3.3 Paper: Recovering intrinsic scene characteristics from images
Feb 10 The structure of images lecture3.ppt Rick’s book, sections 3.4
Feb 15 NO CLASS  
Feb 16 The structure of images lecture4.ppt Subband transforms
Steerable filters
Statistical models of images
Feb 17 Textures end lecture4.ppt


Forsyth&Ponce, chapter 9 Pset 1 due at beginning of class: send me a pdf by email. Bela Julesz. Textons
Heeger & Bergen
Portilla & Simoncelli
Efros & Leung
Feb 22 Objects lecture6.ppt Rick’s book, section 14.2 Boosting
Viola & Jones

A simple boosted detector
Feb 24 Scenes lecture7.ppt Problem set 2.pdf

(due on Wed., March 10)

Spatial pyramid matching
Mar 1 Scenes lecture8.ppt Rick’s book, section 14.4  
Mar 3 Context lecture9.ppt Rick’s book, section 14.5   Review of context
Place recognition
Objects in context
Mar 8 Invariant features lecture10.ppt Rick’s book, section 4.1   SIFT
Mar 10 Edges lecture11.ppt Rick’s book, section 4.2 Pset 2 due Pedestrian detection
Mar 15 Segments lecture12.ppt Rick’s book, chapter 5   A first spectral method
Comparison of methods
Mar 17 Faces lecture13.ppt Rick’s book, sections 14.1, 14.2    
Mar 29 3D lecture14.ppt Rick’s book, section 2.1
Forsyth&Ponce, 1.1, chapter 2
Mar 31 3D lecture15.ppt Exam 1midterm.png Single view metrology
Automatic Photo Pop-up
April 5 3D & stereo lecture16.ppt (.pdf) Rick’s book, section 11.1 Exam 1 due  
April 7 Bayes lecture17.ppt (.pdf)   Send project proposal (one powerpoint slide)  
April 12 Project’s spotlights     Every student does 1 minute presentation of the project  
April 14 MRF’s lecture18.ppt (.pdf)     Intro graphical models
April 19 NO CLASS   Patriots day    
April 21 Motion estimation lecture19.pdf (Ce Liu) Rick’s book, section 8.1 Invited lecturer: Ce Liu  
April 26 Motion estimation lecture20.pdf (Ce Liu)   Exam 2  
April 28 Bayes lecture21.ppt (.pdf)   Exam 2 due  
May 3 You choose lecture22.ppt (.pdf)      
May 5 PRESENTATIONS 5 minutes/presentation 1pm – 4pm Project presentations. Note that this class is twice as long. Send me email if you can only make it for the first part.  
May 10 NO CLASS        
May 12 NO CLASS   Last day classes Project report due (6 pages)  

Related courses:

Other resources:


Here there are links to useful code:

Other useful code:




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