2026 (Current Year) Faculty Courses School of Engineering Department of Systems and Control Engineering Graduate major in Systems and Control Engineering
Visual and Knowledge Information Processing
- Academic unit or major
- Graduate major in Systems and Control Engineering
- Instructor(s)
- Rei Kawakami
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- SCE.I435
- Number of credits
- 100
- Course offered
- 2026
- Offered quarter
- 4Q
- Syllabus updated
- Apr 7, 2026
- Language
- English
Syllabus
Course overview and goals
This course is about information processing of knowledge, especially about computer vision. The classical methods of information acquisition, which are now often estimated by neural networks based on learning, will be studied, leading to a deeper understanding of events related to cameras, images, geometry, and reflective properties. Topics include image feature extraction, 3D shape reconstruction, object tracking, generative modeling, and reinforcement learning.
Course description and aims
To be able to explain and implement basic topics about computer vision and reinforcement learning.
Keywords
Computer vision, reinforcement learning, image features, object recognition, image segmentation, epipolar geometry, 3D restoration, optical flow, object tracking, image-based rendering, generative modeling
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures, programming exercises and paper presentation.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Neural networks |
Topics include multi-layer perceptron, error backpropagation, convolutional neural networks, transformer, and network optimization. |
| Class 2 | Image features and object recognition |
Topics include Harris corner detector, scale selection, SIFT, HOG, ORB, and object detection. |
| Class 3 | Object detection, metric learning, image segmentation |
Topics include face detection, identify recognition, pedestrian detection, gestalt principle, K-means/Mean-shift clustering, graphcut, superpixels. |
| Class 4 | Epipolar geometry, 3D reconstruction |
Topics include epipolar geometry, essential matrix, fundamental matrix, robust estimation, Structure from Motion (SfM). |
| Class 5 | Optical flow, object tracking |
Topics include optical flow, aperture problem, tracking, particile filter、data association. |
| Class 6 | Special imaging devices, generative models |
Topics include plenoptic function, light field, high-dynamic range imaging, neural rendering, generative models. |
| Class 7 | Reinforcement learning, Vision-Language-Action models |
Topics include Markov decision process, optimal action value function, Q learning, and VLA. |
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.
Textbook(s)
None.
Reference books, course materials, etc.
Computer Vision: Algorithms and Applications
Evaluation methods and criteria
Students’ understanding of the lecture content and their ability to apply it will be evaluated based on three components: attendance in each class, presentations introducing research papers, and critiques of those presentations.
Related courses
- ART.T551 ： Image and Video Recognition
- ART.T463 ： Computer Graphics
- XCO.T489 ： Fundamentals of Artificial Intelligence
- ART.T547 ： Multimedia Information Processing
- SCE.I352 ： Fundamentals of Machine Learning
- SCE.I501 ： Image Recognition
Prerequisites
No course requirements.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
reikawa[at]sc.eng.isct.ac.jp