Many of the following slides are modified from
the excellent class notes of similar courses offered in other schools by Prof
Yung-Yu Chuang,
Fredo Durand,
Alexei Efros,
William Freeman,
James Hays,
Svetlana Lazebnik,
Andrej Karpathy,
Fei-Fei Li,
Srinivasa Narasimhan,
Silvio Savarese,
Steve Seitz,
Noah Snavely,
Richard Szeliski, and Li 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.
June 6, 2016: The course is now complete. Please contact Kavita Bala for more information.
This schedule is tentative.
All dates for lectures and unreleased assignments and homeworks are provisional.
Class
| Date
| Topic/notes
| Readings
| Assignments, etc.
|
1 |
Jan 27 |
Introduction and Overview [ppt|pdf] |
Szeliski 1 |
|
2 |
29 |
Image filtering [ppt|pdf] |
Szeliski 3.1 |
|
3 |
Feb 1 |
Image filtering 2 [ppt|pdf] |
Szeliski 3.2 |
|
4 |
3 |
Fourier Analysis [ppt|pdf] |
Szeliski 3.4, 2.3.1 |
|
5 |
5 |
Pyramids [ppt|pdf] |
Szeliski 3.5 |
PA1 out |
6 |
8 |
NumPy, SciPy, PA1 [pdf] |
|
|
7 |
10 |
Image Resampling and Edge Detection [ppt|pdf] |
Szeliski 3.5, 4.2 |
|
8 |
12 |
Edge Detection [ppt|pdf] |
Szeliski 4.1 |
PA1 due |
Feb 15 |
Winter Break |
9 |
17 |
Feature Detection [ppt|pdf] |
Szeliski 4.1 |
|
10 |
19 |
Harris Corner Detector [ppt|pdf] |
Szeliski 4.1 |
|
11 |
22 |
Valve Visitor |
|
HW1 out |
12 |
24 |
Invariance, blob detection, and MOPS [ppt|pdf] |
Szeliski 4.1 |
PA2 out |
13 |
26 |
Feature descriptors and matching [ppt|pdf] |
Szeliski 6.1 |
|
14 |
29 |
Feature matching and transforms [ppt|pdf] |
Szeliski 6.1 |
|
15 |
Mar 2 |
Image transformations [ppt|pdf] |
Szeliski 3.2 |
|
16 |
4 |
Image alignment [ppt|pdf] |
Szeliski A.2, 6.1 |
|
17 |
7 |
RANSAC and Hough Transforms [ppt|pdf] |
Szeliski 6.1 |
|
18 |
9 |
Cameras [ppt|pdf] |
Szeliski 2.1.3-2.1.6 |
|
19 |
11 |
Cameras II [ppt|pdf] |
Szeliski 2.1.3-2.1.6 |
PA2 due |
20 |
14 |
Panoramas 1 [ppt|pdf] |
Szeliski 9 |
HW1 due |
21 |
16 |
Panoramas 2 [ppt|pdf] |
Szeliski 9 |
|
22 |
18 |
Post-Prelim |
|
PA3 out |
Mar 17 | Prelim: 7:30pm, Location: KND116 (Kennedy Hall, 116) |
23 |
21 |
Single-view modeling I [ppt|pdf] |
Szeliski 9 |
|
24 |
23 |
Single-view modeling II [ppt|pdf] |
Szeliski 9 |
|
25 |
25 |
Two-view stereo I [ppt|pdf] |
Szeliski 7.2 |
|
Mar 28 |
Spring Break |
Mar 30 |
Spring Break |
Apr 1 |
Spring Break |
26 |
4 |
Two-view stereo II [ppt|pdf] |
Szeliski 7.2 |
|
27 |
6 |
Two-view stereo III [ppt|pdf] |
Szeliski 7.1-7.4 |
|
28 |
8 |
Photometric stereo I [ppt|pdf] |
Szeliski 12.1.1 |
|
29 |
11 |
Photometric stereo II [ppt|pdf] |
Szeliski 12.1.1 |
PA3 due |
30 |
13 |
Multi-view stereo [ppt|pdf] |
Szeliski 11.6 |
|
31 |
15 |
Structure from Motion [pdf] |
Szeliski 7.1-7.4 |
PA4 out 4/14, HW2 out |
32 |
18 |
Intro to Recognition [ppt|pdf] |
Szeliski 14 |
|
33 |
20 |
Recognition Basics [pdf] |
Karpathy Notes: classification |
|
34 |
22 |
Linear Classifiers and ConvNets 1 [pdf] |
|
|
35 |
25 |
ConvNets 2: Optimization and Backprop [pdf] |
|
|
36 |
27 |
ConvNets 3 [pdf] |
|
PA4 due 4/26, PA5 out 4/26 |
37 |
29 |
ConvNets 4 [pdf] |
|
|
38 |
May 2 |
ConvNets 5 [pdf] |
|
|
39 |
4 |
ConvNets 6 and Research [training, grokstyle] |
|
HW2 due |
40 |
6 |
Research |
|
|
41 |
9 |
Recognition Wrapup [pdf] |
|
|
42 |
11 |
Conclusions [pdf]
|
|
PA5 due 5/10 |
May 22 | Final Exam: Sunday, 2:00pm, BTN100WEST (Barton Hall 100 West-Main Floor) |
|