Computer Vision
Prof. Thomas Brox|
Computer vision is a very active research field with many practical applications, for instance in quality control, robotics, driver assistance systems, and many more. The ultimate goal of computer vision is to imitate the great capabilies of the human visual system, allowing the computer not only to record images but also to interpret them. Significant progress has been made in recent years. This course introduces the most important concepts in today's Computer Vision research. The exercises will consist of programming assignments in Python, where you will learn to implement and/or test the most important techniques presented in the lectures. The lecture was planned to be held partially online and partially in presence. Due to a too small room and far more students being interested in attending the lecture life than in earlier years, in addition to the pandemics getting again more severe in Germany, it must stay online. If you want to connect with other students in presence, you can use one of the rooms in building 101 and watch the online lecture from there. A group of students planned to meet before the lecture in the main hall of the building. Please check the procedure for using these rooms. Particularly, due to the critical situation in hospitals, you must be vaccinated to use these rooms and you must wear a mask.
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Material:
Lecture material will be updated during the course. Some of the recordings are a bit outdated. Recording files are about 200MB each. *denotes lectures with an exercise.| Class 1 (19.10.): | Introduction | Recordings | Online lecture |
| Class 2 (26.10.)*: | Diffusion filters, TV minimization | Recordings | Online lecture |
| Class 3 (2.11.): | Image segmentation and combinatorial optimization | Recordings | Online lecture |
| Class 4 (9.11.): | Spectral clustering | Recordings | Online lecture |
| Class 5 (23.11.)*: | Deep learning I | Recordings | Online lecture |
| Class 6 (30.11.)*: | Deep learning II | Recordings | Online lecture |
| Class 7 (7.12.): | Optical Flow I | Recordings | Online lecture |
| Class 8 (14.12.)*: | Optical Flow II | Recordings | Online lecture |
| Class 9 (21.12.): | Video segmentation | Recordings | Online lecture |
| Class 10 (11.1.)*: | 3D geometry and camera calibration | Recordings | Online lecture |
| Class 11 (18.1.)*: | Stereo reconstruction | Recordings | Online lecture |
| Class 12 (25.1.)*: | Disparity estimation and scene flow | Recordings | Online lecture |
| Class 13 (1.2.)*: | Structure from motion | Recordings | Online lecture |
| Class 14 (8.2.): | Deep learning III | Recordings | Online lecture |
Exercise material:
Short introduction to gitSetup instructions
Exercises on github