▶Japanese
Post to SNS
- Home
- > School of Computing
- > Graduate major in Artificial Intelligence
- > Fundamentals of Computer Vision
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Kawakami Rei Sato Ikuro
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue7-8() Fri7-8()
- Group
- -
- Course number
- ART.T467
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 1Q
- Syllabus updated
- 2022/3/16
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
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.
Student learning outcomes
- 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.
Course taught by instructors with work experience
| ✔ Applicable | How instructors' work experience benefits the course |
|---|---|
| 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 that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Lectures and programming exercises.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| 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 |
Out-of-Class Study Time (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.
Assessment criteria and methods
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 (i.e., required knowledge, skills, courses, etc.)
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.
