Image and Video Processing
This course provides a comprehensive introduction to image and video processing techniques, covering fundamental concepts and advanced applications. Students will learn about image representation, enhancement, filtering, transformation, restoration techniques, along with video compression, motion estimation, and object tracking. The course emphasizes both theoretical foundations and practical implementations using programming tools such as Python or Matlab.
By the end of the course, students will be able to:
1. Understand the principles of digital image and video processing.
2. Apply filtering, transformation, and enhancement techniques to images.
3. Analyze and process video sequences for motion tracking and compression.
4. Develop real-world applications in fields such as computer vision and multimedia systems.
This course is ideal for students in math, computer science, electrical engineering, and related fields who wish to gain expertise in image and video analysis.
By the end of the course, students will be able to:
1. Understand the principles of digital image and video processing.
2. Apply filtering, transformation, and enhancement techniques to images.
3. Analyze and process video sequences for motion tracking and compression.
4. Develop real-world applications in fields such as computer vision and multimedia systems.
This course is ideal for students in math, computer science, electrical engineering, and related fields who wish to gain expertise in image and video analysis.
讲师
日期
2025年04月08日 至 06月04日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周二,周四 | 15:20 - 17:50 | A7-306 | ZOOM 05 | 293 812 9202 | BIMSA |
修课要求
Basic knowledge of optimization and neural networks
课程大纲
1. Introduction to Image and Video Processing
- Digital image representation
- Basics of video signals
2. Image Enhancement and Filtering
- Spatial and frequency domain processing
- Histogram equalization, smoothing, and sharpening
3. Image Transforms
- Fourier transform, Discrete Cosine Transform (DCT)
- Wavelet transform
4. Edge Detection and Feature Extraction
- Sobel, Canny, and Laplacian filters
- SIFT, SURF, and HOG descriptors
5.Image Segmentation
- Thresholding, region-based, and clustering methods
- Morphological Image Processing: erosion, dilation, and shape analysis
6.Image Reconstruction
- Image denoising
- Image deblurring
- Image super-resolution
7.Video Processing Basics
- Motion estimation and optical flow
8.Video Compression Techniques
- H.264, MPEG, and HEVC standards
9.Video Image Reconstruction
- Video denoisng
- Video enhancement
10.Object Tracking in Videos
- Kalman filter, Mean-Shift, and Deep Learning-based tracking
11.Deep Learning for Image and Video Processing
- CNNs, GANs, and transformer models for image and video tasks
12.Applications and Case Studies in Computer Vision
- Implementing real-world image and video processing applications
- Digital image representation
- Basics of video signals
2. Image Enhancement and Filtering
- Spatial and frequency domain processing
- Histogram equalization, smoothing, and sharpening
3. Image Transforms
- Fourier transform, Discrete Cosine Transform (DCT)
- Wavelet transform
4. Edge Detection and Feature Extraction
- Sobel, Canny, and Laplacian filters
- SIFT, SURF, and HOG descriptors
5.Image Segmentation
- Thresholding, region-based, and clustering methods
- Morphological Image Processing: erosion, dilation, and shape analysis
6.Image Reconstruction
- Image denoising
- Image deblurring
- Image super-resolution
7.Video Processing Basics
- Motion estimation and optical flow
8.Video Compression Techniques
- H.264, MPEG, and HEVC standards
9.Video Image Reconstruction
- Video denoisng
- Video enhancement
10.Object Tracking in Videos
- Kalman filter, Mean-Shift, and Deep Learning-based tracking
11.Deep Learning for Image and Video Processing
- CNNs, GANs, and transformer models for image and video tasks
12.Applications and Case Studies in Computer Vision
- Implementing real-world image and video processing applications
参考资料
[1] Rafael, C. Gonzalez, and E. Woods Richard. Digital image processing, Fourth edition. Pearson education, 2018.
[2] Classical and latest related papers
[2] Classical and latest related papers
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
公开
语言
中文