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

Students are expected to have basic math knowledge such as linear algebra, probability theory, and optimization. Programming skills are also required although there is no restriction on specific languages and computing environments.

Midterm exam (25%) / Final exam (25%) / Final project (40%)
The percentages are subject to change.

1. Computer Vision: Algorithms and Applications, Richard Szeliski. (Online version is available at POSTECH library.)
2. Computer Vision, A Modern Approach, Forsyth, Ponce, Pearson Education Inc.

- 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

As announced by the office of academic affairs, this course will be given in an offline manner (501, Cheong-am Library), but operated online only for the first two weeks of this semester. The online classes of this course will be given in a virtual class room via Zoom, a video conferencing app coupled with PLMS. You may have access to the virtual class room by clicking the "Virtual Class Room" icon on the "Course Summary" section of the course webpage on PLMS.

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