2023년도 2학기 컴퓨터 비전 (CSED539-01) 강의계획서

1. 수업정보

학수번호 CSED539 분반 01 학점 3.00
이수구분 전공선택 강좌유형 강의실 강좌 선수과목
포스테키안 핵심역량
강의시간 월, 수 / 14:00 ~ 15:15 / 청암학술정보관 멀티미디어 교육실(Ⅰ) [501호] 성적취득 구분 G

2. 강의교수 정보

곽수하 이름 곽수하 학과(전공) 인공지능대학원
이메일 주소 mercury3@postech.ac.kr Homepage https://suhakwak.github.io/
연구실 COMPUTER VISION LAB. 전화 054-279-2390
Office Hours

3. 강의목표

This course introduces fundamental problems in computer vision and solutions to them. For most of the topics, classical methods and their deep learning counterparts will be presented together. As this is an introductory course, more emphasis will be given on practical solutions rather than theoretical ones.

4. 강의선수/수강필수사항

Students are expected to have basic math knowledge such as linear algebra, probability theory, and calculus. Programming skills are also required. In particular, students are expected to be familiar with deep learning libraries and Linux environment for their final projects, there is no restriction on specific languages and computing environments in the course though. Please note that this course will not provide programming tutorials as its targets are graduate and senior undergrad students.

5. 성적평가

Midterm exam (40%) / Final project (60%)
The percentages are subject to change.

6. 강의교재

도서명 저자명 출판사 출판년도 ISBN

7. 참고문헌 및 자료

There is no regular textbook, but the following will be useful as supplementary materials.
1. Computer Vision: Algorithms and Applications by R. Szeliski (available online)
2. Computer Vision, A Modern Approach by D. Forsyth, J. Ponce (available at POSTECH library.)

8. 강의진도계획

- Course Overview
- Preliminary: Probability and Linear Algebra
- Image Representation and Classification
- Object Detection
- Semantic Segmentation
- Metric Learning and Image Retrieval
- Video Representation and Classification
- Object Tracking
- Matching and Fitting
- Camera Models
- 3D Geometry

9. 수업운영

This course will be given offline (501, Cheong-am Library), but a few classes could be given in forms of pre-recorded videos or in an online manner (e.g., zoom) with prior notices.

10. 학습법 소개 및 기타사항

11. 장애학생에 대한 학습지원 사항

- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등

- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등

- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청