2024 Faculty Courses School of Computing Department of Computer Science Graduate major in Artificial Intelligence
Computer Vision
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Ikuro Sato / Satoshi Ikehata / Yusuke Sekikawa
- Class Format
- Lecture (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue / 7-8 Fri
- Class
- -
- Course Code
- ART.T467
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 1Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
Computer vision is a field that harnesses the power of computers to extract information of interest from visual data captured by sensors. This course offers an introduction to techniques for understanding the shapes, motions, and meanings of objects depicted in images. Its goal is to lay the groundwork for exploring advanced topics in computer vision, such as AI-driven image recognition and image generation.
Course description and aims
- To be able to explain and implement methods of filtering, 3D reconstruction, and object tracking.
- To be able to explain methods of image retrieval, image recognition, and their applications.
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 instructors have conducted R&D about computer-vision technologies such as 3D reconstruction, object tracking, image retrieval, image recognition, and image segmentation in the automotive industry.
Keywords
Filtering, Optical Flow, Epipolar Geometry, Stereo Matching, SLAM, Robust Estimation, Camera Calibration, Kalman Filter, Particle Filter, Principal Component Analysis, Singular Value Decomposition, Subspace Method
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Instructor will use slides and sample programs in the lectures.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Image Processing | Image Acquisition, Geometric Transformation, Resampling, Encoding |
| Class 2 | Filtering | Spatial Filtering, Spectral Filtering, Template Matching |
| Class 3 | Programming: Filtering | Implementation of filtering with MATLAB |
| Class 4 | 3D Reconstruction (1/3) | Optical Flow, Projective Transformation, Singular Value Decomposition, Epipolar Geometry |
| Class 5 | 3D Reconstruction (2/3) | Factorization Method, Rectification, Robust Estimation |
| Class 6 | 3D Reconstruction (3/3) | Bundle Adjustment, Camera Calibration |
| Class 7 | Programming: 3D Reconstruction | Implementation of 3D reconstruction algorithms with MATLAB |
| Class 8 | Object Tracking | Kalman Filter, Particle Filter |
| Class 9 | Programming: Object Tracking | Implementation of object tracking algorithms with MATLAB |
| Class 10 | Image Retrieval | Local Features, Approximate Nearest Neighbor Search |
| Class 11 | Image Recognition | Subspace Method, Support Vector Machine, Feature Aggregation |
| Class 12 | Vision for Autonomous Driving | Recognition, Path Planning, Multi-Task Learning, Reducing Computational Load |
| Class 13 | Event-Based Vision | Odometry Estimation, SLAM |
| Class 14 | Physics-Based Vision | Optical Properties of Object, Photometric Stereo, Shape from Shading |
Study advice (preparation and review)
Students are encouraged to review the materials taught in each class for about 1 hour.
Textbook(s)
None required.
Reference books, course materials, etc.
Lecture slides are used during the class.
Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd ed., Springer, 2011.
Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd ed., Cambridge University Press, 2004.
Evaluation methods and criteria
Presentation video (100%)
Related courses
- ART.T551 ： Image and Video Recognition
- XCO.T489 ： Fundamentals of artificial intelligence
- ART.T547 ： Multimedia Information Processing
- ART.T463 ： Computer Graphics
- ART.T465 ： Sparse Signal Processing and Optimization
Prerequisites
Students are required to have undergraduate-level knowledges on computer science, linear algebra, calculus, probability, and statistics.
Contact information (e-mail and phone) Notice : Please replace from ”[at]” to ”@”(half-width character).
isato[at]c.titech.ac.jp
Office hours
17:05-17:20 on Tuesdays
Other
It is recommended to set up an environment to use MATLAB.