Computer Vision: Algorithms and Applications
This course provides foundational algorithms and practical applications in the fields of image processing and computer vision. Topics covered include the fundamental concepts of image formation, essential techniques in image processing, feature detection methodologies, and classical image reconstruction problems such as denoising, deblurring, super-resolution, etc. Additionally, the course introduces several deep learning-based models in image processing and computer vision.
Lecturer
Date
9th April ~ 2nd July, 2024
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Tuesday,Friday | 10:00 - 11:35 | Online | ZOOM 13 | 637 734 0280 | BIMSA |
Prerequisite
None
Syllabus
1. Introduction and image formation
2. Basic image processing
3. Image reconstruction
4. Deep learning
5. Image compression
6. Image compression
7. Morphological image processing
8. Image segmentation
9. Feature detection
10. KAN and U-KAN
11. Mixed selection
2. Basic image processing
3. Image reconstruction
4. Deep learning
5. Image compression
6. Image compression
7. Morphological image processing
8. Image segmentation
9. Feature detection
10. KAN and U-KAN
11. Mixed selection
Reference
[1] Szeliski, Richard. Computer vision: algorithms and applications, Second edition. Springer Nature, 2022.
[2] Rafael, C. Gonzalez, and E. Woods Richard. Digital image processing, Fourth edition. Pearson education, 2018.
[2] Rafael, C. Gonzalez, and E. Woods Richard. Digital image processing, Fourth edition. Pearson education, 2018.
Audience
Undergraduate
, Advanced Undergraduate
, Graduate
, Postdoc
, Researcher
Video Public
Yes
Notes Public
Yes
Language
Chinese