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 Robust regularization Dense optical flow		Project handout	Good overview article on sampling and particle filters Lecture 23 slides
11/9	Tracking intro		Assignment 3, All problems due 11am Hand-in name: asgn3all Assignment 4 out	Lecture 24 slides
11/11	Particle filtering		Assignment 4, p1 due Handin name: asgn4_p1	Lecture 25 slides
11/13	Finish particle filtering			Lecture 25 slides
11/16	Projects ideas and Binocular Stereo		Bring project ideas to class Assignment 4, all problems due Hand-in name: asgn4_all	Project Ideas
11/18	Stereo		Project proposals Due Hand-in name: proposal	Lecture 26 slides
11/20	Fields of Experts			FoE slides
11/23	Guest Lecture
11/25	Thanks giving recess. No class
11/27	Thanks giving recess. No class
11/30	Fields of Experts
12/2	Special Topic
12/4	Guest Lecture
12/7	Reading week. No class.
12/9	Reading week. No class
12/11	Reading week. No class
12/14			Projects due. Hand-in name: proj