2022 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)
- Rei Kawakami / Ikuro Sato
- Class Format
- Lecture
- 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
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- Jul 10, 2025
- Language
- English
Syllabus
Course overview and goals
Computer vision is a field of research that uses computers to extract information of interest from data acquired by visual sensor. This course introduces the fundamentals of computer vision, including image processing, image features extraction, 3D structure reconstruction, segmentation, and camera calibration. This course aims to develop bases to study advanced computer vision topics such as image recognition and image generation.
Course description and aims
- To be able to explain and implement basic image processing, filtering, feature extraction, and 3D reconstruction methods.
- To be able to explain about image segmentation and camera calibration.
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 been using methods such as feature extraction, 3D reconstruction, and calibration in the automotive industry.
Keywords
Image Processing, Image Filtering, Image Features, Optical Flow, Epipolar Geometry, Stereo Matching, Image Segmentation, Camera Calibration
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures and programming exercises.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction | To understand areas of computer vision |
| Class 2 | Basics of Image Processing | To understand basics of digital image processing |
| Class 3 | Programming: Image Processing | Implementation of basic image processing |
| Class 4 | Filtering | To understand 2D filters (e.g. smoothing, edge extraction) |
| Class 5 | Programming: Filtering | Implementation of basic image filtering |
| Class 6 | Image Features | To understand key point detectors and local descriptors |
| Class 7 | Programming: Image Features | Implementation of a representative key point detector or local descriptor. |
| Class 8 | Optical Flow | To understand basics of optical flow (e.g. Lukas-Kanade method) |
| Class 9 | Epipolar Geometry | To understand epipolar geometry, essential matrix, and motion parameter estimation |
| Class 10 | Stereo Matching | To understand 3D reconstruction from stereo camera |
| Class 11 | Programming: 3D Reconstruction | Implementation of 3D reconstruction method from multiple images |
| Class 12 | Segmentation | To understand segmentation method (e.g. Level Set, Graph Cut) |
| Class 13 | Camera Calibration | To understand extrinsic and intrinsic parameters |
| Class 14 | Discussion: Related Work | To discuss related computer vision papers |
Study advice (preparation and review)
Students are encouraged to review the materials taught in each class for about 60 minutes.
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
Final Report (40%) and 3 short reports (60%)
Related courses
- ART.T552 ： Advanced Topics in Computer Vision
- ART.T551 ： Image and Video Recognition
- ART.T463 ： Computer Graphics
- XCO.T489 ： Fundamentals of artificial intelligence
- ART.T465 ： Sparse Signal Processing and Optimization
- ART.T547 ： Multimedia Information Processing
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; reikawa[at]c.titech.ac.jp
Office hours
11:30-12:00 on Wednesdays
Other
Students are required to set up an environment to use MATLAB and Python.