|
Computer Vision CS
6320, Spring 2018 |
''Man Drawing a Lute'', woodcut by Albrecht Dürer, 1525 Metropolitan Museum of Art, New York |
|
|
|
Lectures: Instructor:
TA: Grading:
Prerequisites:
|
Monday & Wednesday, 1:25 – 2:40 PM, WEB L114 Srikumar Ramalingam, srikumar@cs.utah.edu Office hours: Monday and Wednesday 3:00
-4:00 PM MEB 3464 Programming assignments (60%), Project (20%), Final (20%) linear algebra, vector calculus, C/C++, Matlab, |
Class No
|
Date
|
Title
|
1
|
01/09
|
Introduction
to Computer Vision
|
2
|
01/11
|
Camera
Models and Image Formation
|
-
|
01/16
|
No Class!
|
|
01/17
|
HW1 (PDF, Latex) Released, Due on
01/29
|
3
|
01/18
|
Camera
Pose Estimation and RANSAC
|
|
01/23
|
3D
Reconstruction
|
5
|
01/25
|
Epipolar Geometry
|
6
|
01/30
|
Epipolar Geometry (continued)
|
7
|
02/01
|
The first meeting on projects!
HW2 (PDF,
Latex & Data) Released,
Due on 02/17
|
8
|
02/06
|
Keypoints and Descriptors
|
9
|
02/08
|
Image Matching (Slides, Paper)
|
10
|
02/13
|
An
Introduction to Graphical Models
|
11
|
02/15
|
Belief Propagation
|
|
02/19
|
Project Proposal
Due (Team Members, Problem Statement)
|
-
|
02/20
|
No Class!
|
12
|
02/22
|
Belief Propagation
(continued)
|
|
02/25
|
HW3 (PDF,
Latex), Due on 03/03
|
13
|
02/27
|
Belief
Propagation (continued)
|
14
|
03/01
|
Graph Cuts
|
|
03/05
|
Project Status Due (Introduction and Prior
Art)
|
15
|
03/06
|
Midterm Review
|
16
|
03/08
|
Midterm
|
-
|
03/13
|
No class! (Spring Break)
|
-
|
03/15
|
No class! (Spring Break)
|
17
|
03/20
|
Graph Cuts (continued)
HW4,
Due on 04/02
|
18
|
03/22
|
Using neural nets
to recognize handwritten digits (Slides, Chapter1)
|
19
|
03/27
|
Using neural nets
to recognize handwritten digits (continued)
|
20
|
03/29
|
Project Status
Report and Presentations
Intermediate report
due on 04/07
|
21
|
04/03
|
Project Status
Report and Presentations
|
22
|
04/05
|
How the backpropagation works (Slides, Chapter2)
|
|
04/07
|
HW5,
Due on 04/21
|
23
|
04/10
|
Improving the way the
neural networks learn (Slides,
Chapter3)
|
24
|
04/12
|
Improving the way the
neural networks learn (Slides,
Chapter3)
|
25
|
04/17
|
Visual proof that neural
nets can compute any function
Why are
deep neural networks hard to train
|
26
|
04/19
|
Stereo and
Semantic Segmentation |
27
|
04/24
|
Final Project Presentations (4+2 minutes for each group) |
28
|
04/26
|
|
29
|
05/01
|
Final Exam (6-8 PM)
|
|
05/05
|
Final Project Report Due
|
2.
DLBOOK1: Deep Learning (available online) by Ian Goodfellow,
Yoshua Bengio, and Aaron Courville, An MIT Press book, 2016
3.
DLBOOK2: Neural Networks and Deep Learning
(available online) by Michael Nielson
4.
CVNOTES1: Some lecture notes on geometric
computer vision (available online) by Peter Sturm
Additional
Reading:
The latest
research topics in computer vision can be
learned from the publications in the following computer vision
conferences:
· International Conference on Computer Vision (ICCV)
· Computer Vision and Pattern Recognition (CVPR)
· European Conference on Computer Vision (ECCV)