EE 6485 Computer Vision 計算機視覺

Fall 2017, Wed. 3:30pm to 6:20pm, Location DELTA台達216
Instructor: Min Sun

TAs: 簡廷安 tingan0206@gmail.com
楊皓鈞 chadyoungy@gmail. com
王尊玄 johnsonwang0810@gmail.com
石孟立 shihsml@gmail.com
林沅廷 brade31919@gmail.com

Course Description

Can computers understand the visual world as we could? This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic, statistical, data-driven approaches. Topics include image processing; segmentation, grouping, and boundary detection; recognition and detection; motion estimation and structure from motion. This class will also lead you to the discussion of applications applying state-of-the-art techbiques in recognition, detection, and video analysis.

The course will consist of four programming projects, one final project, and a few self-tutorial sessions (12 minutes for each team of 5 students). Please find information about projects and self-tutorial sessions in the syllabus.

Prerequisites

This course requires programming experience (mainly Python) as well as linear algebra, basic calculus, and basic probability. Previous knowledge of visual computing will be helpful.

Textbook

Readings will be assigned in "Computer Vision: Algorithms and Applications" by Richard Szeliski. The book is available for free online or available for purchase.

Resource

Awesome computer vision github link
Awesome deep learning github link

Grading

Your final grade will be made up from

• 60% 5 programming projects
• 30% final projects (includes proposal, midtern report, project pitch, project presentation, and project report). 3 students maximum (Project Ideas)
• 10% self-tutorial + class participation

You will lose 10% each day for late projects. However, you have three "late days" for the whole course. That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late day. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible.

Important Links:

• Github course repositories: slides, homeworks, etc.

Contact Info and Office Hours:

You can contact the professor with any of the following:

• Min Sun: sunmin@ee.nthu.edu.tw

Office Hours

• Min Sun, 台達管962, 電話 035731058, 時間：Mon. 4:30pm-5:30pm
• 簡廷安和王尊玄, 資電711, 時間：Tue. 3:30pm-4:30pm

Tentative Syllabus

lecture	Class Dates	Topic	Slides	Reading	Extra Info (e.g., Homework/Exam)
1	W, Sept. 13	Introduction to computer vision and cameras & optics	pdf,pdf2	Szeliski 1, 2.1 (especially 2.1.5)
		git and github	pdf		homework 0 out
2	W, Sept. 20	Light and color & Image filtering & Python Tutorial	pdf,pdf2	Szeliski 2.2, 2.3, and 3.2	homework 0 due
3	W, Sept. 27	Thinking in uency	pdf	Szeliski 3.4	homework 1 (hybrid image) out
		Image pyramids and applications &	pdf pdf	Szeliski 3.5.2 and 8.1.1
4	W, Oct. 4	No Class (Mid-Autumn Festival)
5	W, Oct. 11	Continue & Edge detection	pdf	Szeliski 4.2
		Interest points, corners, and local image features	pdf pdf2	Szeliski 4.2
6	W, Oct. 18	Feature matching and hough transform	pdf	Szeliski 4.3	homework 1 due
		Model fitting and RANSAC & self-tutorial	pdf2	Szeliski 4.3	homework 2 (image stitching) out
7	W, Oct. 25	Cancelled due to ICCV	pdf
		Model fitting and RANSAC & self-tutorial	pdf2	Szeliski 4.3
8	W, Nov. 1	Panorama Stitching	pdf	Szeliski 9
		Stereo and Structure from Motion	pdf pdf2	Szeliski 7
9	W, Nov. 8	SfM & cere-solver & self-tutorial	see above	Szeliski 4.1.4 and 8.4	homework 2 due & project proposal due
		Feature Tracking and Optical Flow	pdf	Szeliski 4.1.4 and 8.4	homework 3 (scene recognition) out
10	W, Nov. 15	No Class (Sports Day)
11	W, Nov. 22	Machine learning intro and clustering	pdf
		Machine learning: classification	pdf pdf2
12	W, Nov. 29	Recognition overview, bag of features	pdf	Szeliski 14	homework 3 due
		large-scale instance recognition & self-tutorial	pdf	Szeliski 14.3.2	homework 4 (face detection) out
13	W, Dec. 6	Detection with sliding windows: Viola Jones and Dalal Triggs	pdf pdf2	Szeliski 14.1
		Mixture of Gaussians and advanced feature encoding	pdf
14	W, Dec. 13	Modern Object Detection: DPM &self-tutorial	pdf		homework 4 due
		Modern Object Detection: Selective Search	pdf		homework 5 (deep classification) out
15	W, Dec. 20	Deep Learning: introduction	pdf pdf
		Deep Learning: CNN	pdf		midterm project report due
16	W, Dec. 27	Deep Learning: recent work	pdf1 pdf2		homework 5 due
		Deep Learning: object detector	pdf
17	W, Jan. 3	Project presentation: 10 teams
18	W, Jan. 10	Project presentation: 5 teams	pdf
	Th, Jan. 11				final project report due

Project Proposal Format:

- max 4 pages;
- 3 sections:

• title and authors
• sec 1. intro: problem you want to solve and why
• sec 2. technical part: how do you propose to solve it?
• sec 3. milestones (dates and sub-goals)
• references

- final format: pdf, please!

Project Progress (mid-term) Report Format:

- max 4 pages;
- 3 sections:

• title and authors
• sec 1. intro: problem you want to solve and why
• sec 2. technical part: how do you propose to solve it?
• sec 3. milestones achieved so far
• sec 4. remaining milestones (dates and sub-goals)
• references

- CVPR final format: pdf, please!

Project Final Report Format:

- Max 10 pages;
- Title and authors
- Abstract: short summary of the project with main results
- 6 sections:

• Sec 1. Introduction: introduce the problem you want to solve, expain why it is important to solve it; and indicate the method you used to solve it. add a concept figure showing the overall idea behind the method you are presenting.
• Sec 2.1. Review of previous work (i.e. previous methods that have explored a similar problem)
• Sec 2.2. Say why your method is better than previous work; and/or summarize the key main contributions of your work;
• Sec 3.1: Technical part: Summary of the technical solution
• Sec 3.2: Technical part: Details of the technical solution; you may want to decompose this section into several subsections; add figures to help your explanation.
• Sec 4: Experiments: present here experimental results of the method you have implemented with plots, graphs, images and visualizations.
• Sec 5: Conclusions: what's the take home message?
• Sec 6: References

- CVPR final format: pdf, please!
You can look at one of the recent instructor publications (such as this) as an example.

Project Report Evaluation:

- Your project report will be evaluated based on the quality of the writing, the clarity of your technical explanation and, overall, how well you get your message across. If you follow the structure above, you'll have good chances to do a good job. :)

Project Source Code:

There is no need to attach a print out of the source codes to the manuscript. Final source codes of your working program need to be shared with TA and the instructor on github (open or private repos are fine); this file is due on the project submission deadline date.

Project Presentation in Class:

- The presentation must be at most 15 minutes long. Please see below for detailed presentation guidelines,

Presentation format:

Your slides should consist of a title slide, followed by slides that discuss the following aspects of your project:

• Problem Motivation/Description
• Technical Approach
• Some Results

Please do not plan on using more than 5-6 slides.

Evaluation:

- Your team will be evaluated based on the clarity of the presentation, quality of the slides, how well you get your message across, and how well you handle the questions at the end. Note that the presentation can still contain ongoing/preliminary results; final results may be included in the final report. - We will use a peer-review system.

Acknowledgements:

The materials from this class rely significantly on slides prepared by other instructors, especially James Hayes, Fei-Fei Li, and Silvio Savarese. Each slide set and assignment contains acknowledgements.