進階的影像處理課程

Graph Algorithms
1. What is Graph
2. Paths, Cycles, Trials, Trees
3. Eulerian Graphs
4. Minimum Spanning Tree
5. Shortest Paths
6. Maximum Network Flows
7. Maximum Matchings
8. Independent Sets and Covers
9. Dominating Sets
10. Maximum Bipartite Matching
11. Stable Matching
12. Connectivity
13. Vertex Colorings
14. Planar Graphs

ref. Book

"Introduction to Graph Theory", 2nd Editon, by Douglas B. West
"Introduction to Algorithms", 3rd Editon, by Cormen, Leiserson, Rivest, Stein

Computer Vision
Lecture 1 Cameras
Lecture 2 Camera Models and Calibration
Lecture 3 Projective Geometry
Lecture 4 Stereo Correspondence
Lecture 5 3D Reconstruction
Lecture 6 Fundamental Matrix Computation
Lecture 7 Feature Detection and Matching
Lecture 8 Feature-based Alignment
Lecture 9 Structure from Motion
Lecture 10 Planes & Homographies

ref. Book

1. Computer Vision: A Modern Approach, 2003
Written by D. A. Forsyth and J. Ponce, and published by Pearson Education, Inc
2. Multiple View Geometry in Computer Vision, 2004
Written by Hartley, R.~I. and Zisserman, A., Cambridge University Press, ISBN: 0521540518
3. Computer Vision: Algorithms and Applications, 2010
Written by R. Szeliski


Pattern Recognition:

1.Introduction;
2.Bayes Decision Theory;
3.Maximum-Likelihood and Bayesian Parameter Estimation;
4.Nonparametric Techniques;
5.Linear Discriminant Functions;
6.Multilayer Neural Networks;
7. Nonmetric Methods;
8. Algorithm Independent Machine Learning ;
9.Unsupervised Learning and Clustering.

ref. Book

[1]"Pattern Classification", by Richard O. Duda, Peter E. Hart and David G. Stork, John Wiley & Sons, 2nd edition, 2001.
[2]"Introduction to Statistical Pattern Recognition", by Keinosuke Fukunaga, 2nd Edition, Academic Press, 1990.
[3]"Statistical Pattern Recognition", by Andrew Webb, Second Edition, John Wiley & Sons, 2002.


Advanced Computer Graphics

1. Mathematical fundamentals of computer graphics
2. Data representation for 3D models
3. Vectors and computer graphics
4. Three-dimensional model permutation
5. Project 1: 3D model permutation with probability
6. Three-dimensional model disturbing
7. Three-dimensional model manipulation
8. Project 2: 3D model manipulation and probability

ref. Book

1. Z. N. Li and M. S. Drew, Fundamentals of Multimedia, Pearson Pretice Hall, 2004.
2. A. Watt, 3D Computer Graphics, Third Edition, Addison-Wesley, 2000.
3. I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich, and T. Kalker, Digital
Watermarking and Steganography, Morgan Kaufmann, 2004.
4. N. C. Huang, M. T. Li, and C. M. Wang, "Toward Optimal Embedding capacity for
Permutation Steganography," IEEE Signal Processing Letters, Vol. 16, No. 9, pp.
802-805, 2009.

Advanced Rendering Techniques

1. Introduction
2. Light and Color
2-1 Radiometry and photometry
2-2 Color Spaces
2-3 Standard RGB color spaces
3 HDR Image Encodings
3-1 LDR versus HDR encodings
3-2 HDR image formats
3-3 HDR encoding comparison
4 The Human Visual System and HDR Tone Mapping
4-1 Tone-mapping concepts
4-2 Visual adaptation models for HDR one mapping
4-3 Tone-mapping software
5 HDR applications
5-1 statics steganographic application
5-2 dynamic steganographic application

ref. Book

E. Reinhard, G. Ward, and S. Pattanaik, High Dynamic Range Imaging: Acquisition, Display, and Image-based Lighting, Morgan Kaufmann Publishers, 2005.
R. Fattal, D. Lischinski, and M. Wermann, ACM Transactions on Graphics, Vol. 21. No. 3, pp. 249-256, 2002.
R. Mantiuk, K. Myszkowski, and H. P. Seidel, A Perceptual Framework for Contrast Processing of High Dynamic Range Images, ACM Transactions on Applied Perception, Vol. 3, NO. 2, pp. 286-308, 2006.
E. Reinhard, E. A. Khan, A. O. Akyuz, and G. M. Johnson, Color Imaging: Fundamentals and Applications, AK Peters, 2008.
K. Myszkowski, R. Mantiuk, and G. Krawczyk, High Dynamic Range Video, Morgan & Claypool Publishers, 2008.
B. Hoefflinger, High-Dynamic-Range (HDR) Vision: Microelectronics, Image Processing, Computer Graphics, Springer, 2007.
I. J. Cox, M. L. Miller, J. A. Bloom, J. Fridrich, and T. Kalker, Digital
Watermarking and Steganography, Morgan Kaufmann, 2004.

Video Signal Processing

Lecture 1 Introduction to Digital Video Processing
Lecture 2 Video Sampling and Interpolation
Lecture 3 Motion Detection and Estimation
Lecture 4 Video Enhancement and Restoration
Lecture 5 Video Stabilization and Mosaicing
Lecture 6 Video Segmentation
Lecture 7 Motion Tracking in Video
Lecture 8 Basic Transform Video Coding
Lecture 9 MPEG-1 and MPEG-2 Video Standards
Lecture 10 MPEG-4 Visual and H.264/AVC: Standards for Modern Digital Video
Lecture 11 Video Quality Assessment
Lecture 12 A Unified Framework for Video Indexing, Summarization, Browsing, and Retrieval
Lecture 13 Video Communication Networks
Lecture 14 Video Security and Protection
Lecture 15 Wireless Video Streaming
Lecture 16 Video Surveillance
Lecture 17 Face Recognition from Video

ref. Book

The Essential Guide to Video Processing, 2009
Written by Al Bovik, and published by Elsevier.