This is a tentative schedule and subject to change.
| Date | Lecture Description | Readings | Assignments | Materials |
| 9/3 | Introduction. Class ends early (11:30) for commencement ceremonies. | Lecture 1 slides | ||
| 9/5 | Introduction continues.
What is vision good for? Why is it hard? Why is it interesting? How do you pose the problem computationally? |
Assignment 0 out | Lecture 2 slides | |
| 9/8 | Continuing Introduction.
Case study - object recognition. |
Ch 1.1, 1.4, Ch 4. | Lecture 3 slides | |
| 9/10 |
Convolution and linear filtering. |
Ch 7.4-7.7, 9.2 | ||
| 9/12 | Pyramids and image derivatives | Assignment 0
due
Assignment 1 out |
||
| 9/15 | First derivatives and edges. | Lecture 6
slides
Matlab code - derivatives of Gaussians Change
blindness |
||
| 9/17 |
Gradients and Laplacian pyramid. Linear algebra tutorial. CIT 165 (Motorola), 6:30PM - 7:30PM |
|
Lecture 7 slides | |
| 9/19 | Filtering
and features
Linear algebra tutorial. CIT 165 (Motorola), 6:30PM - 7:30PM |
Extra reading: feature detection: Distinctive Image Features from Scale-Invariant Keypoints (read Sections 1-3.1) | Asgn1 - Problem 1 and 2 Due Hand-in name: asgn1_p1_p2 |
Lecture 8 slides |
| 9/22 | Images as vectors. Appearance-based models | Ch 22.3 | Lecture 9 slides | |
| 9/24 | Covariance and PCA | Extra Chapter | Lecture 10 slides | |
| 9/26 | PCA and SVD | Asgn1 - Problem 3 and 4 Due Hand-in name: asgn1all
|
Lecture 11 slides | |
| 9/29 | Finish PCA applications. | Ch 22.1, 22.2 | Assignment 2 p1 p2 out | Lecture 12 slides |
| 10/1 | Review of basic probability.Multivariate Gaussians, covariance, probability. | Lecture 13 slides | ||
| 10/3 | Guest Lecture: Real application of PCA. Modeling human body shape. Alex Balan | Ch 15 | Asgn2 Problem 1 due Hand-in name: asgn2_p1 |
|
| 10/6 | Finish multivariate Gaussians and PCA | Asgn 2 (problem 3) out | Lecture 14 slides | |
| 10/8 | Motion Intro | Moghaddam & Pentland | Asgn2 Problem 2 due Hand-in name: asgn2_p2 |
Lecture 15 slides |
| 10/10 |
Guest Lecture: Joe Mundy |
class notes on motion |
|
|
| 10/13 | University Holiday, No class | |||
| 10/15 |
Guest Lecture: Moldovan, Denoising old movies. |
Moldovan et al. | Assign2 Problem 3 due
Hand-in name: asgn2all |
|
| 10/17 | Guest Lecture: Gabriel Taubin |
|
||
| 10/20 |
Affine Motion |
Assignment 3 out | Lecture 16 slides | |
| 10/22 | Finish affine motion |
Lecture 17 slides | ||
| 10/24 | Cameras and projection | Lecture 18 slides | ||
| 10/27 |
Robust estimation |
Reading: Robust statistics and optical flow |
Assignment 3, Problems 1 & 2 due Hand-in name: asgn3_p1_p2 |
Lecture 19 slides |
| 10/29 | Robust estimation II, Non-linear optimization | Lecture 20 slides | ||
| 10/31 |
Guest Lecture: Moldovan, Super Resolution |
Reading: Super Resolution, Irani and Peleg |
|
Super Resolution slides |
| 11/3 |
Robust regularization |
Reading: Horn and Schunck, Determining Optical Flow |
Assignment 3, All problems due Hand-in name: asgn3all
|
Lecture 21 slides |
| 11/5 | Dense optical flow Start tracking |
Good overview article on sampling and particle filters | ||
| 11/7 | Tracking intro |
|
Lecture 23 slides | |
| 11/10 | Particle filtering |
Assignment 4, Problem 1due |
Lecture 24 slides | |
| 11/12 | Finish particle filtering. More project ideas |
Project handout |
|
|
| 11/14 | Project ideas and Binocular Stereo | Bring project ideas to class | Project Ideas | |
| 11/17 |
Guest Lecture: Bill Warren |
Assignment 4, all problems due |
||
| 11/19 | Stereo | Project proposals Due Hand-in name: proposal |
Lecture 26 slides | |
| 11/21 | Fields of Experts | Fields of Experts | FoE slides part 1 and 2 | |
| 11/24 | Fields of Experts | |||
| 11/26 | Thanksgiving recess. No Class | |||
| 11/28 | Thanksgiving recess. No Class | |||
| 12/1 | Learning optical flow | |||
| 12/3 | Modern object recognition | |||
| 12/5 | The future | |||
| 12/8 | Reading week No class | |||
| 12/10 | Reading week. No class | |
||
| 12/12 | exam period (no exam) Project due. | Part based recognition: Fergus, Perona & Zisserman |
Projects due. |