Computer Vision 1 (Winter Term 2025/2026)
Overview
    - Course consisting of:
    
        - Lectures (2 hours/week) in BEY/0E40/H (George-Bähr-Str. 1) on Mondays, 11:10–12:40
        
- Exercise group sessions (2 hours/week)
        
            - In APB/E065/P on Tuesdays, 13:00–14:30 
            
- In APB/E065/P on Tuesdays, 14:50–16:20  
            
- In APB/E067/P on Thursdays, 11:10–12:40
            
- In APB/E067/P on Thursdays, 13:00–14:30
            
- Online on Wednesdays, 13:00–14:30
        
 
- Self-study
        
- Final examination
    
 
- Enrolment (OPAL). Additional rules for enrolment may apply, depending on the study programme.
Contents
- Introduction
- Operators on digital images
- Digital images
- Point operators 
- Linear operators (esp. convolution)
- Non-linear operators, including edge and corner detection
 
- Classification of digital images
- Logistic regression
- Gradient descent algorithm
- Deep learning for computer vision (basics)
- Backward propagation algorithm
 
- Segmentation of digital images
- Image segmentation as a (lifted) multicut problem
- Semantic segmentation as a node labeling (lifted) multicut problem
- Local search algorithms
 
- Object recognition in digital images
- Single object recognition as a partial quadratic assignment problem            
- Multiple object recognition as a node labeling multicut problem
 
- Object tracking in digital images
- Single object tracking as coupled partial quadratic assignment problems
- Multiple object tracking as an integer linear program
 
- Linear and integer optimization for computer vision
- Introduction
- Simplex algorithm
- Branch-and-bound/branch-and-cut algorithms