Course title
視覚信号処理特論Ⅰ   [Advanced Visual Signal ProcessingⅠ]
Course category courses for the doctral program  Requirement   Credit 1 
Department   Year   Semester 3rd 
Course type 3rd  Course code 125027
Instructor(s)
田中 雄一   [TANAKA Yuichi]
Facility affiliation Faculty of Engineering Office afjgxte/L1151  Email address

Course description
This 1-credit course introduces theory and applications of emerging tools for signal processing on graphs, including a review of spectral graph theory, filtering of graph signals, downsampling, and wavelets and filter banks.
Expected Learning
Learners who successfully complete this course will be able to:

1. Understand the motivation of graph signal processing
2. Understand basic spectral graph theory
3. Understand and implement a few operations of graph signal processing
Course schedule
1. Introduction of graph signal processing
2. Review of spectral graph theory and linear algebra
3. Adjacency matrix and graph Laplacian
4. Graph signals and graph Fourier transform
5. Graph filtering
6. Downsampling and oversampling of graph signals
7. Applications for image processing
Prerequisites
Students entering this class are assumed to have had linear algebra and signal processing in undergraduate level. A basic knowledge of graph theory is plus. Basic skills of programming language, e.g, MATLAB, python, or C++ (OpenCV) are required.
Required Text(s) and Materials
Handouts are provided.
References
G. Strang, Linear algebra and its applications, 4th ed., Cengage Learning, 2005.
F. R. Chung, Spectral graph theory, volume92, AMS Bookstore, 1997.
D. K. Hammond, P. Vandergheynst, and R. Gribonval. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis , 30(2):129-150, 2011.
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. Signal Processing Magazine, IEEE , 30(3):83-98, 2013.
D. Spielman, Spectral graph theory, Lecture Notes, Yale University, 2009.
Assessment/Grading
Midterm report: 30%
Final report: 70%
Message from instructor(s)
Course keywords
Office hours
Remarks 1
Remarks 2
Related URL
Lecture Language
Japanese
Language Subject
Last update
3/15/2019 12:26:07 PM