Multi-Dimensional Signal and Image Processing
- 2012 年度版 (2013年度版準備中)
Instructor
Goal and Theme
To understand fundamentals and algorithms of multi-dimensional signal and image processing, and to apply them to own research.
Abstract
This course discusses on methods not only for extracting signal component from observed data but also for processing multi-dimensional image data. Goal of the course is to understand past and newer algorithms for processing multi-dimensional signal and image data. Students will be given lectures in the first half, and will join reading and explaining in turn in the second half.
Schedule
Spring
| 回 | テーマ | 内容 |
|---|---|---|
| 1 | Multi-dimensional signal and image | Structure of image, sampling theorem, and interpolation |
| 2 | Statistical methods and some algorithms 1 | Multi-spectral image, spectral vector, inner and outer product, variance covariance matrix |
| 3 | Statistical methods and some algorithms 2 | Principal component analysis, canonical correlation analysis and multiple regression analysis |
| 4 | Supervised classification | Maximum likelihood method and binary decision tree classifier |
| 5 | Un-supervised classification | k-means classtering, ISODATA and Binary devision classtering altorithm |
| 6 | Image registration 1 | Tie-point search, triangulation and piece-wise Affine transformation |
| 7 | Image registration 2 | Optical flow vector and its application to the image registration |
| 8 | Temporal change detection 1 | Fundamentals of temporal change, image normalization and change detection using PCA |
| 9 | Temporal change detection 2 | Change detection using multiple regression (CDMR) and paticullar change extractor (PACE) |
| 10 | Stereo pair image processing | Perspective transformation, relative orientation and absolute orientation |
| 11 | Medical image processing | Computed tomography (CT), helical CT image and application to lung cancer screening |
| 12 | Topics 1 and/or students presentation | Image restoration and SAR image processing |
| 13 | Topics 2 and/or students presentation | Morphing and its application |
| 14 | Topics 3 and/or students presentation | Face image processing |
| 15 | Topics 4 and/or students presentation | Video image processing |
授業外に行うべき学習活動
Reading papers related to topics in each lecture. Preparing reports to be submitted.
Materials
Given via network
References
・Ronald N. Bracewell : Fourier Analysis and Imaging, Kluwer Academic / Plenum, 2003 ・B.Girod, G.Greiner and H.Niemann (Ed.) : Principles of 3D Image Analysis and Synthesis, Kluwer Academic Publishers, 2002 ・N.Nikolaidis and I.Pitas : 3-D Image Processing Algotirhms, Wiley, 2001 ・D.Caramella and C.Bartolozzi (Ed.) : 3D Image Processing – Techniques and Clinical Applications, Springer, 2002 ・O.Faugeras and Q.T.Luong : The Geometry of Multiple Images, MIT Press, 2001 ・J.R.Jensen : Introductory Digital Image Processing 3rd. Edition – A Remote Sensing Perspective, Pearson Prentice Hall, 2005
Evaluation Method
Quality of report and presentation.
情報機器使用
Students are expected to bring their note PC to read online PDF files.
前年度の授業改善アンケートからの気づき
Nothing.