CS4670/5670 Lectures, Spring 2015
Alexei Efros,Andrej Karpathy Fei-Fei LiSrinivasa NarasimhanSteve Seitz,Noah Snavely,Richard Szeliski, andLi Zhang. The instructor is extremely thankful to the researchers for making their notes available online. Please feel free to use and modify any of the slides, but acknowledge the original sources where appropriate.
| Class | Date | Topic/notes | Readings | Assignments, etc. | |
|---|---|---|---|---|---|
| 0 | Jan 21 | Introduction and Overview [ppt|pdf] | Szeliski 1 | ||
| 1 | 23 | No class | |||
| 2 | 26 | Image filtering [ppt|pdf] | Szeliski 3.1, 3.2 | ||
| 3 | 28 | Image filtering [ppt|pdf] | Szeliski 3.1, 3.2 | ||
| 4 | 30 | Edge detection and Image Resampling [ppt|pdf] | Szeliski 4.2 | ||
| 5 | Feb 2 | Image Resampling and PA 1 (Intelligent Scissors) [ppt|pdf] | Szeliski 2.3.1 and 3.5 | PA1 out | |
| 6 | 4 | Image Interpolation [ppt|pdf] | Szeliski 2.3.1 and 3.5 | ||
| 7 | 6 | Feature detection [ppt|pdf] | Szeliski 4.1 | ||
| 8 | 9 | Harris corner detection [ppt|pdf] | Szeliski 4.1 | HW1 out | |
| 9 | 11 | Invariance, blob detection, and MOPS [ppt|pdf] | Szeliski 4.1 | ||
| 10 | 13 | Feature descriptors [ppt|pdf] | Szeliski 4.1 | PA1 due 2/12 (9:00am) | |
| Feb 16 | Winter Break | ||||
| 11 | 18 | Feature matching and transformations [ppt|pdf] | Szeliski 6.1 | ||
| 12 | 20 | Image transformations [ppt|pdf] | Szeliski 3.2 | ||
| 13 | 23 | Image alignment and PA 2 [ppt|pdf] | Szeliski A.2, 6.1 | PA2 out | |
| 14 | 25 | RANSAC and Hough Transforms [ppt|pdf] | Szeliski 6.1 | ||
| 15 | 27 | Mid-term review | HW1 due 2/26 | ||
| 16 | Mar 2 | Cameras [ppt|pdf] | Szeliski 2.1.3-2.1.6 | ||
| 17 | 4 | Projection I [ppt|pdf] | Szeliski 2.1.3-2.1.6 | ||
| 18 | 6 | Post-Prelim | |||
| Mar 5 (Thu) | Prelim:7:30 pm, Location: Call Auditorium, Kennedy Hall | ||||
| 19 | 9 | Projection II [ppt|pdf] | Szeliski 9 | PA2 due | |
| 20 | 11 | Panoramas [ppt|pdf] | Szeliski 9 | PA3 out | |
| 21 | 13 | Single-view modeling I [ppt|pdf] | Szeliski 9 | ||
| 22 | 16 | Single-view modeling II [ppt|pdf] | Szeliski 9 | ||
| 23 | 18 | Two-view stereo I [ppt|pdf] | Szeliski 7.2 | ||
| 24 | 20 | Two-view stereo II [ppt|pdf] | Szeliski 7.2 | ||
| 25 | 23 | Two-view stereo III [ppt|pdf] | Szeliski 7.1-7.4 | ||
| 26 | 25 | Photometric stereo [ppt|pdf] | Szeliski 12.1.1 | PA3 due | |
| 27 | 27 | Dragon Day (No class. Support the Dragon!) | |||
| Mar 30 | Spring Break | ||||
| Apr 1 | Spring Break | ||||
| Apr 3 | Spring Break | ||||
| 28 | 6 | Multi-view stereo [ppt|pdf] | Szeliski 11.6 | HW2 out | |
| 29 | 8 | Structure from Motion [ppt|pdf] | Szeliski 7.1-7.4 | PA4 out | |
| 30 | 10 | Intro to Recognition [ppt|pdf] | Szeliski 14 | ||
| 31 | 13 | Recognition Basics [ppt|pdf] | Karpathy Notes:classification | ||
| 32 | 15 | Recognition Basics 2 [ppt|pdf] | Karpathy Notes:linear classification | ||
| 33 | 17 | Backprop [ppt|pdf] | Karpathy Notes:optimization,optimization 2 | ||
| 34 | 20 | CNNs 1 [pdf] | Karpathy Notes:Neural Nets 1,2 | ||
| 35 | 22 | CNNs 2 [pdf] | Karpathy Notes:Neural Nets 3,CNNs | PA4 due,PA5 out | |
| 36 | 24 | CNNs 3 [pdf] | Karpathy Notes:CNNs | ||
| 37 | 27 | Charter Day (no class) | |||
| 38 | 29 | CNNs 4 (slides posted together with previous lecture) | HW2 due | ||
| 39 | May 1 | Large-scale Datasets [pdf] | |||
| 40 | 4 | Recognition Wrapup [pdf] | PA5 due | ||
| 41 | 6 | Conclusions | |||
| May 14 | Final Exam: 9am, OLH155: Olin Hall 155 | ||||