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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
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
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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
- 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 Information and Communications Engineering
- Instructor(s)
- Miyata Takamichi
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Day/Period(Room No.)
- Intensive ()
- Group
- -
- Course number
- ICT.S403
- Credits
- 2
- Academic year
- 2022
- Offered quarter
- 2Q
- Syllabus updated
- 2022/8/17
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course focuses on the processing technologies for multi-dimensional information. Topics include sampling and quantization of multi-dimensional information, compression coding (entropy coding, quantization error analysis, orthogonal transform, Karhunen-Loeve transform (KLT), and Discrete Cosine Transform (DCT)), recent advances in image processing (image segmentation, colorization, image editing, and image retargeting), and image restoration via convex optimization (convex function/set, convex programming algorithms and regularization methods for image processing). The course enables students to understand the mathematical tools widely applicable to solve the real-world information processing problems.
Student learning outcomes
By the end of this course, students will:
1. Understand the fundamental of image coding methods.
2. Explain how to extract the essential and mathematical problems from real-world image processing problems.
3. Acquire the fundamentals of convex optimization
4. Apply mathematical tools for wide variety of multi-dimensional information processing problems.
Keywords
Signal processing, image processing, convex optimization
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
To allow students to get a good understanding of the course contents, all course materials are provided on the lecturer's web-site. The additonal description is provided at the lecture.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Guidance | Understand the course objectives. |
| Class 2 | Quantization, sampling, sampling theorem | Understand the sampling theorem |
| Class 3 | Entropy, source coding theorem | Undestand the fundamental of compression coding. |
| Class 4 | Quantization, analysis of quantization error | Understand the statistical analysis of quantization error. |
| Class 5 | Orthogonal transform，KLT (Karhunen-Loeve transform) | Understand the optimality of KLT |
| Class 6 | From KLT to DCT (Discrete Cosine Transform) | Understand the relationship between DCT and KLT. |
| Class 7 | Application of eigenvalue problem，Locally linear embedding, Normalized cuts | Understand the applications of eigenvalue problem. |
| Class 8 | Colorization using optimization, Poisson image editing | Understand that the simple system of linear equations can be used for solving the image processing problems. |
| Class 9 | Image retargetting，Seam carving, Bidirectional similarity | Understand the difficulties of image retargeting problem and how to solve them. |
| Class 10 | Image recovery via convex optimization 1, Least square method，Tikhonov regularization | Understand the regulazaition technique and its necesity. |
| Class 11 | Image recovery via convex optimization 2, convex function, convex set, gradient descent | Understand the fundamentals of convex optimization |
| Class 12 | Image recovery via convex optimization 3, TV regularization，norm，Legendre-Fenchel transform | Understand the complex regularization term. |
| Class 13 | Image recovery via convex optimization 4, mixed-norm ，Chambolle's algorithm | Understand the numerical algorithms of convex optimization |
| Class 14 | Image mosaicing and homography | Understand the basic 3D image transform. |
Textbook(s)
Not specified
Reference books, course materials, etc.
All course materials are provided on the lecturer's web-site.
Assessment criteria and methods
Overall learning achievement is evaluated based on written report on the recent advances in image processing (100%).
Related courses
- ICT.S206 ： Signal and System Analysis
- ZUS.F301 ： Foundations of Functional Analysis
- ICT.S414 ： Advanced Signal Processing (ICT)
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Not specified.
Other
In 2022, this course is to be presented as a 5 days intensive lecture (8/31[3-8], 9/1[3-8], 9/2[3-8], 9/5[3-8], 9/6[3-6]) in the summer vacation period.
