Date

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 Matlab code - edges 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