CSI 4116: Computer Vision

Course Information

Lecture Dates: Tuesday 10:00 - 10:50, Thursday 10:00 - 11:50

Class Location: 공학원 170E

Instructor: Seong Jae Hwang (seongjae@yonsei.ac.kr)

Office: 공학원 449d


Course Description

In this class, students will learn the basics of modern computer vision. The course will first cover low-level computer vision topics such as image filtering, edge detection, feature extraction, description and matching, grouping, and clustering. Then, we will cover high-level topics such as object detection, object recognition, segmentation, unsupervised learning, and generative models. We will cover recently popular techniques such as convolutional and recurrent neural networks. We will also discuss a few topics from recent computer vision conferences.

After the course, you may be able to:


Required: Object-Oriented Programming and Data Structures. Recommended: Linear Algebra basics.

Programming languages


There are recommended readings from the following textbooks:

You can also refer to the following textbooks for additional explanations:

Course Policies

Grading (Tentative)

Homework Submission


Homework Late Policy

Collaboration Policy and Academic Honesty

Note on Disabilities

If you have a disability for which you are or may be requesting accommodation, you are encouraged to contact your instructor ASAP.

Note on Medical Conditions

If you have a medical condition which will prevent you from doing a certain assignment, you must inform the instructor of this before the deadline. You must then submit documentation of your condition within a week of the assignment deadline.

Statement on Classroom Recording

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student's own private use.

Course Schedule

CSI 4116 - Computer Vision

Academic integrity

All assignment submissions must be the sole work of each individual student. Students may not read or copy another student's solutions or share their own solutions with other students. Students may not review solutions from students who have taken the course in previous years. Submissions that are substantively similar will be considered cheating by all students involved, and as such, students must be mindful not to post their code publicly. The use of books and online resources is allowed, but must be credited in submissions, and material may not be copied verbatim. Any use of electronics or other resources during an examination will be considered cheating. 

If you have any doubts about whether a particular action may be construed as cheating, ask the instructor for clarification before you do it. The instructor will make the final determination of what is considered cheating. 

Cheating in this course will result in a grade of F for the course and may be subject to further disciplinary action. 

Using an open-source codebase is accepted, but you must explicitly cite the source, especially following the owner's guidelines if it exists. For any writing involved in the project, plagiarism is strictly prohibited. If you are unclear whether your work will be considered plagiarism, ask the instructor before submitting or presenting the work. 


This course is consistent with the previous Intro to Computer Vision courses at Pitt by Adriana Kovashka and Seong Jae Hwang which were inspired by the following courses:


Some computer vision datasets:

Some code and frameworks of interest: