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
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Materials
Below you find all the slides, recordings, and exercise materials for this course. Note that the materials are being updated on the fly. Especially many of the recordings are outdated and will only be updated during the course. Recordings are about 250MB each; some are much bigger due to videos.| 15.6. | Class 1: Introduction and image basics | Recording |
| 17.6. | Class 2: Noise, basic operators, and convolutional filters | Recording |
| 22.6. | First exercise session | 24.6. | Class 3: Optimization | Recording |
| 29.6. | Class 4: Deep learning on images | Recording |
| 1.7. | Class 5: Motion estimation | Recording |
| 6.7. | Exercise session | |
| 8.7. | Class 6: Feature matching and feature learning | Recording |
| 13.7. | Class 7: 3D reconstruction | Recording |
| 15.7. | Class 8: Recognition and segmentation | Recording |
| 20.7. | Class 9: Unsupervised learning and mechanistic analysis | Recording |
| 22.7. | Final exercise session |
There is a forum for discussion, which will be available from June 16.