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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
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- Common courses
- School of Materials and Chemical Technology
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- 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
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School of Environment and Society
- Department of Architecture and Building Engineering
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- 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 Systems and Control Engineering
- Instructor(s)
- Tanaka Masayuki
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(S516)
- Group
- -
- Course number
- SCE.I501
- Credits
- 1
- Academic year
- 2019
- Offered quarter
- 4Q
- Syllabus updated
- 2019/3/18
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
Computational imaging systems have variety of applications include consumer cameras, cell phone cameras, vehicle camera systems, surveillance, medical imaging, remote sensing, and human computer interaction. Topics of computational imaging have a wide range of technologies in computer vision and image processing. Recently, the network-based image processing become hot topic. This course focuses on the network-based image processing. In this course, students develop and train the network by themselves.
Student learning outcomes
Students are expected to
(i) gain an ability to build and learn deep neural networks,
(ii) gain an ability to use numerical computing environments using MATLAB to solve engineering problems,
(iii) gain practical skill to apply the deep learning techniques such as momentum, data arugumentation and filter setting, after taking this course.
Keywords
Computational imaging, Image processing, Convolutional neural network (CNN), Deep learning, matlab
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This class is a kind of active learning. Instructor will give some information, but students are required to develop their own matlab code.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction of this course and of grader system. | Logical operation by neural network |
| Class 2 | Two-layer logical network & simple image processing | Two-layer logical network & simple image processing |
| Class 3 | Introduction of train1000 project: train the network with 1000 samples. | Train 1000 project |
| Class 4 | Introduction of BlockScramble challenge. Type-I: Supervised learning Type-II: Unspervised learning | Develop and train own network. |
| Class 5 | Key techniques of CNN | Develop and train own network. |
| Class 6 | Evaluation data submission | Evaluation data submission |
| Class 7 | Presentation1 | Presentation |
| Class 8 | Presentation2 | Presentation |
Textbook(s)
None
Reference books, course materials, etc.
None
Assessment criteria and methods
Presentation, and report.
Related courses
- SCE.I531 ： Computer Vision
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
Other
Students will code by themselves.
