2025 (Current Year) Faculty Courses School of Computing Department of Computer Science Graduate major in Artificial Intelligence
Fundamentals of Computer Vision
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Nakamasa Inoue / Ikuro Sato
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Tue (M-B43(H106)) / 3-4 Fri (M-B43(H106))
- Class
- -
- Course Code
- ART.T475
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 1Q
- Syllabus updated
- Apr 2, 2025
- Language
- English
Syllabus
Course overview and goals
Computer vision is a field that leverages the power of computers to extract meaningful information from visual data captured by sensors. This course provides an introduction to techniques ranging from basic image processing to image recognition and generation. In particular, various deep learning-based methods are covered through lectures and exercises.
Course description and aims
At the end of this course, students should be able to
- explain and implement methods for filtering and tracking.
- explain basic concepts of image recognition and generation.
- use computer-vision models appropriately with deep learning libraries.
Student learning outcomes
実務経験と講義内容との関連 (又は実践的教育内容)
The instructor has been conducting research and development in computer vision technologies, including object tracking, image retrieval, image recognition, image segmentation, and 3D reconstruction, particularly in the automotive industry (Dr. Sato).
Keywords
Filtering, Tracking, Deep Learning, Image Recognition, Image Generation
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
- Students will understand the basics of computer vision includuding implementation.
Class flow
Slides and sample programs are used in the lecture.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Image Processin |
Image Acquisition, Geometric Transformation, Resampling, Encoding |
| Class 2 | Filtering |
Spatial Filtering, Spectral Filtering, Template Matching |
| Class 3 | Exercise: Filtering |
Implementation of filtering algorithms with MATLAB |
| Class 4 | Object Tracking |
Kalman Filter, Particle Filter |
| Class 5 | Exercise: Object Tracking |
Implementation of object tracking algorithms with MATLAB |
| Class 6 | Image Retrieval |
Local Features, Approximate Nearest Neighbor Search |
| Class 7 | Basics of Deep Learning |
Fundamentals of Deep Neural Networks |
| Class 8 | Image Classification |
Convolutional Neural Networks |
| Class 9 | Loss and Optimization |
Training methods |
| Class 10 | Exercise: Image Classification |
Implementation of Convolutional Neral Networks |
| Class 11 | Object Detection |
Region Proposal Networks |
| Class 12 | Image Segmentation |
Fully Convolutional Networks |
| Class 13 | Image Generation |
Adversarial Training |
| Class 14 | Comprehensive Exercise |
Deep Learning Applications |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class. They should do so by referring to textbooks and other course material.
Textbook(s)
None required.
Reference books, course materials, etc.
R. Szeliski, Computer Vision: Algorithms and Applications, 2nd Ed., Springer, 2022.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learing, MIT Press, 2016.
D. Foster, Generative Deep Learning, O'Reilly Media, 2019.
Evaluation methods and criteria
Presentation video (100%)
Related courses
- XCO.T489 ： Fundamentals of artificial intelligence
- ART.T547 ： Multimedia Information Processing
- ART.T463 ： Computer Graphics
- ART.T465 ： Sparse Signal Processing and Optimization
- ART.T476 ： Advanced Topics in Computer Vision
Prerequisites
Students are required to have undergraduate-level knowledges on computer science, linear algebra, calculus, probability, and statistics.
Office hours
Students can contact the instructor after class.