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Image Processing and Computer Graphics

Adam Kortylewski, Max Argus, Prof. Matthias Teschner

Image Processing and Computer Graphics have impact not only in computer science but also in other research areas, such as biology or medicine. Image processing is important in robotics and many industrial applications. Much of modern machine learning has been developed on image data. Computer graphics dominates the movie theaters. This course gives a broad overview of these fields and introduces some basic techniques. It is highly recommended to take this course before attending other classes in computer vision or computer graphics. Consequently, if you think about specializing in these fields, you should take this course as early as possible. The exercises are intended to give you a better understanding of the most important techniques you learn in class. You are supposed to implement some selected methods in C/C++ and develop an intuition of their usage.

The lecture for the Image Processing part is planned to be given in presence. Nonetheless, there is a good tradition to record the lecture, so that you can also participate remotely. The exercise sessions will be via an online meeting for everybody can participate, but also a room is reserved for that time, i.e., you can meet there to communicate with fellow students. You must be present for the exam.

For details on the Computer Graphics part, please refer to the site of Prof. Teschner

Lecture:
(2 SWS)
Tuesday 14:15-15:45 and Wednesday 10:15-11:45
Room: 101-00-036

Exercises:
(2 SWS)
Wednesday 10:00-11:00
Online meeting in Zoom. The passcode is u7d0d8csp
Contact persons (Image Processing only): Simon Schrodi, Arian Mousakhan
Reserved room for meetup: 076-01-007 (GoogleMaps shows the wrong building!)
Beginning (Image Processing): Tuesday, June 13

ECTS-Points: 6
Recommended Semester:   4-6 (Bachelor), 1-2 (Master)
Requirements: Fundamental mathematical knowledge and basic programming skills in C/C++

Exam: The exam (written exam) is on tbd.
A test exam including the solution is available.

Further Remarks: All course material is in English.

Object segmentation

Materials

Below you find all the slides, recordings, and exercise materials for this course. Note that the materials are being updated on the fly, so only the next upcoming classes will be up-to-date. Recordings are about 250MB each; some are much bigger due to videos. The German recordings are from few years ago and do not perfectly match the structure of the course anymore.

DateTopicSlidesRecordings Exercises

11.6. Class 1: Introduction and image basics Slides Class 1 English
12.6. Class 2: Noise, basic operators and filters Slides Class 2 English | German 19.6. | Material
18.6. Class 3: Energy minimization Slides Class 3 English | German
25.6. Class 4: Variational methods Slides Class 4 English | German
26.6. Class 5: Motion estimation Slides Class 5 English | German 3.7. | Material
2.7. Class 6: Matching and local descriptors Slides Class 6 English | German 17.7. | Material
9.7. Class 7: 3D reconstruction Slides Class 7 English | German
10.7. Class 8: Recognition and Deep Learning Slides Class 8
16.7. Class 9: Segmentation Slides Class 9 English | German

There is a forum for discussion, which will be available from June 11.