2022 Faculty Courses School of Computing Department of Computer Science Graduate major in Artificial Intelligence
Advanced Topics in Computer Vision
- Academic unit or major
- Graduate major in Artificial Intelligence
- Instructor(s)
- Rei Kawakami / Ikuro Sato / Yusuke Sekikawa
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Tue / 7-8 Fri
- Class
- -
- Course Code
- ART.T552
- Number of credits
- 200
- Course offered
- 2022
- Offered quarter
- 2Q
- 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 special topics about image understanding and image generation. This course aims to develop ability to study frontiers of computer vision researches.
Course description and aims
- To be able to explain and implement basic object recognition and tracking methods.
- To be able to explain about the selected topics covered in this course.
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 of image recognition in the automotive industry.
Keywords
Image Retrieval, Generic Object Recognition, Convolutional Neural Networks, SLAM, Image Based Rendering, Computational Photography, Autonomous Driving, Event Based Camera
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 | Image Retrieval | To understand Bag-of-Words and approximate nearest neighbor search |
| Class 2 | Generic Object Recognition | To understand generic object recognition using local descriptors |
| Class 3 | Programming: Object Recognition | Implementation of a basic object recognition method |
| Class 4 | Object Tracking | To understand object tracking using time series filters |
| Class 5 | Programming: Object Tracking | Implementation of Object Tracking using a time series filter |
| Class 6 | Convolutional Neural Networks | To understand weight sharing, pooling, and error backpropagation |
| Class 7 | Programming: Neural Networks | Implementation of neural network |
| Class 8 | Visual SLAM (Simultaneous Localization And Mapping) | To understand optimization of location and 3D shapes |
| Class 9 | Image-based Rendering | To understand reflection models, BRDF, and light field. |
| Class 10 | Computational Photography | To understand super resolution and deblurring |
| Class 11 | Machine Learning for Vision | To understand types of machine learning (e.g. supervised learning, unsupervised learning, reinforcement learning) |
| Class 12 | Visual Recognition for Autonomous Driving | To understand recognition tasks needed for autonomous driving |
| Class 13 | Event-based Camera | To understand the basics and applications of event-based camera |
| Class 14 | Discussion: Frontiers of Computer Vision | To discuss current issues in computer vision |
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.T467 ： Fundamentals of Computer Vision
- ART.T551 ： Image and Video Recognition
- CSC.T439 ： Augmented Reality
- 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 knowledges on fundamentals of computer vision, 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 environment to use MATLAB and Python.