Computer Vision 2024

Fall 2024, Monday 1:10pm to 4:00pm, Classroom: C0206R (Second Medical Science Building 206)
Instructor: Chih-Yuan Yang

Course Information

In this course, I will introduce fundamental concepts of computer vision. I expect students can understand a few important topics in computer vision such as image processing, color spaces, recognition, convolutional neural networks, generative models, vision-language models. Through discussion in the class and implementation for projects, students should learn concepts and first-hand experience of applying computer vision knowledge for real-world applications.

The course consists of four programming projects and one final group project (max 5 members each team). Please find information about final project in the syllabus.

Prerequisites

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

Syllabus

Week	Date	Topic	Slides	Recording	Action
1	9/2	Introduction to Computer Vision, Image Formation	pptx	YouTube
2	9/9	Image Processing	pptx	YouTube
3	9/16	Model Fitting and Optimization	pptx	YouTube	Team member list
4	9/23	Deep Learning Part 1	pptx	YouTube	Homework1 due
5	9/30	Presentation for Homework 1			Substituted by Prof. Albert Ma, project proposal
6	10/7	Project pitch, ECCV’24 Experience and Deep Learning Part 2	pptx	YouTube	Project pitch
7	10/14	Kubeflow and Deep Learning Part 3	pptx	YouTube	Homework2 due
8	10/21	Presentation for Homework 2
9	10/28	TENNs-PLEIADES, MediaPipe Hand, Cascaded Classifiers	pptx	YouTube
10	11/4	CNN Visualization, Adversarial Examples, Self-Supervised Learning, Image Classification	pptx	YouTube	Homework3 due
11	11/11	ControlNet installation, Presentation for Homework 3	pptx	YouTube	Project midterm report
12	11/18	no lecture			School sports compensatory leave
13	11/25	Image Generation 1	pptx	YouTube	Homework4 due
14	12/2	Presentation for Homework 4
15	12/9	Medical AI Forum
16	12/16	Final presentation			Final presentation
17	12/23	Flexible learning week, Image Generation 2 (optional online course)	pptx	YouTube	Final report due

Term Project Topics, Slides, and Reports

Topic	Slides	Report	Code
Acupoint PC6 Localization	pptx	pdf	py
Solving Visual Puzzles Using Large Language Models	pptx	pdf
Human Violence Detection Using Videos	pdf	pdf
Brain Tumor Segmentation	pdf	docx
Hand Gesture Recognition	pptx	docx

Lecturer’s comments on the term project reports docx

Textbook

• Computer Vision: Algorithms and Applications by Richard Szeliski (2022)
  It is available online for free.

Reference Books

• Available online for free offered by the authors.
  • Programming Computer Vision with Python: Tools and algorithms for analyzing images by Jam Solem. (2012)
  • Deep Learning by Ian Goodfellow et al. (2016). A third-party-made PDF is available at GitHub.
  • Computer Vision: Models, Learning, and Inference by Simon J.D. Prince. (2012)
  • Data Driven Science and Engineering by Steven L. Brunton and J. Nathan Kutz. (2017)
  • Learn Computer Vision Using OpenCV With Deep Learning CNNs and RNNs by Sunila Gollapudi (2019)
• No free PDF offered by the authors, but available at school library.
  • Digital Image Processing 3th edition by Rafael Gonzalez and Richard Woods. (2008) There is a 4th edition published in 2017.
  • Digital Image Processing using Matlab 2nd edition by Rafael Gonzalez et al. (2010)
  • Learning OpenCV3 by Adrian Kaehler & Gary Bradski. (2017)
• No free PDF offered by the authors, but code available at GitHub.
  • OpenCV 3.x with Python By Example 2nd edition by Gabriel Garrido and Prateek Joshi. (2018)

Existing Full-length Course Lecture Recordings

• MIT 6.S191: Introduction to Deep Learning 2023
• NTHU EE 6485 Computer Vision 2022
• UMich EECS 498-007: Deep Learning for Computer Vision 2019
• MIT 6.S094: Deep Learning for Self-Driving Cars 2018
• Stanford CS231n: Deep Learning for Computer Vision 2017
• UCF CAP5415: Computer Vision 2014

Existing Online Lecture Videos for Computer Vision Knowledge Points

• Columbia Computer Science 2021

Existing Computer Vision Course Slides for Self-Learning

• Alexei Efros at UC Berkeley https://cs280-berkeley.github.io/
• Derek Hoiem at UIUC https://courses.engr.illinois.edu/cs543/sp2017/
• David Forsyth at UIUC http://luthuli.cs.uiuc.edu/~daf/courses/CV23/planned.html
• James Hays at Georgia Tech https://faculty.cc.gatech.edu/~hays/compvision2022fall/
• Steve Seitz at U Washington https://courses.cs.washington.edu/courses/cse576/20sp/calendar/
• Min Sun at NTHU https://aliensunmin.github.io/teaching/cv2022/index.html
• Justin Johnson at UMich https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/

Grading

Your final grade will be made up from

• 40% 4 pieces of homework
• 60% term project, including proposal (5%), project pitch (10%), midterm report (10%), final project presentation (20%), and final project report(15%). Maximum 5 members each group.
• late policy
  I do not have a strict late policy because there are only less than 20 students in this course. I will directly ask students why I do not see their submissions via Teams messages.

Contact Info and Office Hour

Chih-Yuan Yang: cyyang@cgu.edu.tw
Office hours: Tue 10:30~11:30 Management Building Room 1416