This is a tentative schedule and subject to change.
Date  Lecture Description  Readings  Assignments  Materials 
9/9  Introduction.  Lecture 1 slides  
9/11  Introduction continues.
What is vision good for? Why is it hard? Why is it interesting? How do you pose the problem computationally? 
Reading: Ch 1.1 
Lecture 2 slides


9/14  Continuing Introduction.  Reading: Ch 3.2.1 (linear filtering). Background: 2.3.1, 3.3 

9/16  Case study  object recognition. 
Reading: 3.4.1, 3.4.2 

9/18  Finish intro and case study. Start linear filtering. 

9/21  Convolution and linear filtering. 
Lecture 6 slides  
9/23  Filtering and Gaussian pyramids 
Matlab code  Gaussian smoothing


9/25  Edges, derivatives and Laplacian pyramid 

9/28  Deqing Sun: Gradients, filtering and features (needed for Assignment 1) 
Asgn1  p1 and p2 Due Handin name: asgn1_p1_p2 
Lecture 9 slides  
9/30  Tim St Claire: data collection for assignment 2  please attend. If you miss this, assignment 2 won't be as fun. 

10/2  Guest Lecture: Silvia Zuffi, Color constancy and the Retinex Model 

10/5  Images as vectors. Appearancebased models  Asgn1 all Due (You can only handin p3 and p4) Handin name: asgn1all 


10/7  Covariance and PCA 


10/9  PCA and SVD  Lecture 12 slides  
10/12  Columbus Day, No class  
10/14  PCA and faces.  Asgn2 p1 p2 out 


10/16  Finish PCA applications. Review of basic probability. Multivariate Gaussians, covariance, probability. 

Lecture 14 slides  
10/19 
Finish multivariate Gaussians and PCA

Moghaddam and Pentland  Lecture 15 slides  
10/21 
Finish covariance, start motion 

Asgn2 p1, p2 due
Handin name: asgn2_p1_p2 

10/23  Motion intro. Assumptions, formalization, Sum of Squared Differences. 


10/26 
Motion estimation. Aperture problem, optical flow constraint equation, optimization, least squares.


Lecture 18 slides  
10/28 
Motion illusions and affine motion 
Asgn3 out  Lecture 19 slides  
10/30  Computing affine motion Incremental warping and coarse to fine. 
Lecture 20 slides  
11/2 
Cameras and projection

Lecture 21 slides  
11/3  Assignment 3, p1 and p2 due 11am Handin name: asgn3_p1_p2 

11/4  Robust estimation, Nonlinear optimization Initial project discussion 


11/6  More on projects Nonlinear optimiation Robust regularization 
Bring project ideas to class 

11/9 
Regularization and Dense optical flow

Assignment 3, All problems due 11am Handin name: asgn3all 

11/11  Dense flow 

Lecture 24 slides  
11/13 
Start Tracking

Project proposals Due Handin name: proposal 
Lecture 25 slides  
11/16 
Particle filtering

Assignment 4, problem 1 due Handin name: asgn4_p1 
Lecture 26 slides  
11/18 
Stereo


Lecture 27 slides  
11/20 
Finish stereo and start object recognition

Lecture 28 slides  
11/23 
Object recognition

Assignment 4, all problems due
Handin name: asgn4_all 
Lecture 29 slides  
11/25 
Thanksgiving recess. No class


11/27 
Thanksgiving recess. No class


11/30  Object recognition  Lecture 30 slides  
12/2  Finish object recognition and concusions  Lecture 31 slides  
12/4  No class That there are known knowns, D. Rumsfeld. 

12/7  Reading week. No class.  
12/9  Reading week. No class  
12/11  Reading week. No class  
12/16  note new date  Projects due. 