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 Assignment 1 p1 p2 out Lecture 3 slides Ball and Shadow movie Illusory motion from shadows 9/16 Case study - object recognition. Reading: 3.4.1, 3.4.2 Lecture 4 slides 9/18 Finish intro and case study. Start linear filtering. Lecture 5 slides Linear algebra tutorial slides Matlab tutorial code 9/21 Convolution and linear filtering. Lecture 6 slides 9/23 Filtering and Gaussian pyramids Lecture 7 slides Matlab code - Gaussian smoothing 9/25 Edges, derivatives and Laplacian pyramid Asgn1 all out Lecture 8 slides Matlabcode - derivatives of Gaussians 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.  Appearance-based models Asgn1 all Due (You can only handin p3 and p4) Hand-in name: asgn1all Lecture 10 slides 10/7 Covariance and PCA Lecture 11 slides 10/9 PCA and SVD Lecture 12 slides 10/12 Columbus Day, No class 10/14 PCA and faces. Asgn2 p1 p2 out Lecture 13 slides 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 Lecture 16 slides Asgn2 p1, p2 due Hand-in name: asgn2_p1_p2 10/23 Motion intro. Assumptions, formalization, Sum of Squared Differences. Lecture 17 slides 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 Hand-in name: asgn3_p1_p2 11/4 Robust estimation, Non-linear optimization Initial project discussion Lecture 22 slides 11/6 More on projects Non-linear optimiation Robust regularization Project handout Bring project ideas to class Project ideas slides 11/9 Regularization and Dense optical flow Assignment 3, All problems due 11am Hand-in name: asgn3all   Assignment 4 out Lecture 23 slides 11/11 Dense flow Lecture 24 slides 11/13 Start Tracking Project proposals Due Hand-in name: proposal Lecture 25 slides 11/16 Particle filtering Assignment 4, problem 1 due Hand-in 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 Hand-in 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, There are things we know that we know, There are known unknowns, That is to say there are things that we now know, we don't know But there are also unknown unknowns, There are things we do not know we don't know And each year we discover a few more Of those unknown unknowns. 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. Hand-in name: proj