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304am永利集团“304永利集团官网入口学子全球胜任力提升计划”研究生系列短课程(2023-001)

发表于: 2023-05-21   点击: 

课程题目:Topics in Image Analysis with Matlab

授 课 人:Young Ju Lee 教授

所在单位:Texas State University

课程时间:2023年05月22日 - 2023年05月29日 18:00-19:00

课程地点:天元研讨室5、6


课程摘要:The course goal is to introduce a couple of topics in image analysis. This includes image segmentation, image de-noising and image reconstruction. An indepth knowledge on some of important topics in image analysis will be presented. The course will be maintained to provide not only algorithmic techniques but also a hands-on experience to implement the algorithms. After students complete the course works, they are expected to have abilities to tackle a number of image analysis problems.


日程安排:

1. (May/22) Introduction of Topics in Image Analysis (Room 5)

2. (May/23) Image Segmentation by Normalized Cut - 2 Way Cut (Room 6)

3. (May/24) Image Segmentation by Constrained Normalized Cut - 2 Way Cut (Room 6)

4. (May/25) Image Segmentation by Multiway Normalized Cut and Multiway Constrained Normalized Cut (Room 5)

5. (May/26) Link between Multiway Constrained Normalized Cut and Algebraic Multigrid Methods (Room 6)

6. (May/27) Multiscale Image Segmentation (Room 6)

7. (May/28) Algebraic Coarsening Algorithm (Room 6)

8. (May/29) Image Denoising by Bilateral Smoothing (Room 5)


基础知识:Advanced Calculus, Linear Algebra and familiarity with differential equations and graph theory. A basic skill to use Matlab is necessary.


授课人简介:Young Ju Lee is a Professor at Texas State University, Mathematics Department. He obtained his Ph.D degree at Penn State and had a prior faculty position at UCLA and Rutgers, The State University of New Jersey. His expertise is at the development of fast solver for partial differential equations. His current research focuses on development of structure preserving finite element discretization for PDE systems. His research has been funded by National Science Foundation and American Chemical Society. The current research is being funded by Korea Brain Pool program by National Research Foundation of Korea.