Course title
視覚信号処理特論   [Visual Signal Processing]
Course category	courses for the doctral program	Requirement		Credit	2
Department		Year	～	Semester	Fall
Course type	Fall	Course code	126806
Instructor(s)
田中 雄一   [TANAKA Yuichi]
Facility affiliation	Faculty of Engineering	Office		Email address

Course description

This course introduces: 1. 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. 2. Multidimensional signal processing and its applications, including multidimensional Fourier transform, isotropic/anisotropic image smoothing, image/video coding.

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 4. Understand fundamentals of multidimensional signal processing 5. Understand typical image smoothing methods 6. Understand algorithms of image/video compression

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 8. Fundamental topics of multidimensional signal processing 9. Filtering of images 10. Frequency domain image filtering 11. Image smoothing: Isotropic filters 12. Image smoothing: Edge-preserving filters 13. Still image compression 14. Video compression

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. Woods, Multidimensional Signal, Image, and Video Processing and Coding, Second Edition, Academic Press, 2011

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

4/10/2017 11:35:55 AM