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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.

Lecture:
(2 SWS)
Tuesday, 10:15-11:45,
Room: 101-01-009/13

Exercises:
(2 SWS)

Thursday, 12:00-13:00 (first session October 27, 2022),
Zoom link
Contact persons: Sudhanshu Mittal, Simon Ging

Beginning: Tuesday, October 18, 2022

ECTS Credits: 6
Requirements: Fundamental mathematical knowledge and programming skills in Python. Image Processing and Computer Graphics teaches important technical knowledge that won't be repeated in Computer Vision. Thus, prior attendance of Image Processing is highly recommended.
Language: The lectures are given in English.
Exam: Written exam.

Motion Segmentation

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 Some of the recordings are a bit outdated.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 (18.10.): Introduction Recordings
Class 2 (25.10.)*: Diffusion filters, TV minimization Recordings
Class 3 (8.11.): Image segmentation and combinatorial optimization Recordings
Class 4 (15.11.): Spectral clustering Recordings
Class 5 (22.11.): Deep learning I Recordings
Class 6 (29.11.): Deep learning II Recordings
Class 7 (6.12.): Optical Flow I Recordings
Class 8 (13.12.): Optical Flow II Recordings
Class 9 (20.12.): Video segmentation Recordings
Class 10 (10.1.): 3D geometry and camera calibration Recordings
Class 11 (17.1.): Stereo reconstruction Recordings
Class 12 (24.1.): Disparity estimation and scene flow Recordings
Class 13 (1.2.): Structure from motion Recordings
Class 14 (8.2.): Deep learning III Recordings


Exercise material:

Short introduction to git
Setup instructions
Exercises on github