The topics are relatively closely related to the lecture. We will have theoretical exercises, where we aim to deepen our understanding of elements within the pattern recognition pipeline. Additionally, we have practical tasks, in order to observe the behavior of the methods on real-world data.

Both exercise sessions cover the same content. A single session will typically take about 45-60 minutes. Exercise sheets will become available on this website.

Older lecture videos are available at (only accessible if you are inside the university network; if you want to watch the videos from home, consider to tunnel the connection). Be aware that the contents of the lecture have changed since then!

Some remarks about programming

We will shortly go over the Python code for the practical exercises in the sessions. The solutions will be provided (password-protected) after another week. The password will be announced in the next sessions, or by request (write me an email).

In general, we advise you to use Python, which is available in the CIP pool including extensions such as OpenCV, but there are also other (open source) possibilities:

- Weka toolbox: Only for feature vectors, cannot handle images. Java-based. To extract features you might in some cases use:

- Fiji: General useful image processing tool with a lot of functionality (provided by research institutions based on plugins)

- InsightToolkit (ITK): C++ based, more Image Processing and less pattern recognition.

- OpenCV (CV = Computer Vision): C++-based. More pattern recognition/computer vision than image processing (basics are available, though). Nice examples, like tracking, included.

If using ITK and/or OpenCV, you can use the free express-editions of Visual Studio (google for download links).

In case of questions, contact me.