||Lern- und Qualifikationsziele
The aim is to acquaint students with the different methods available for image processing and analysis, and to give them hands-on experience in applying the theoretical concepts of the course on real-life applications in a programming project.
Prerequisites are knowledge of undergraduate level of algebra, calculus and preferably Fourier transforms and statistics. Some experience in computer programming is helpful. However the necessary tools will also be taught during the exercises.
Gliederung / Themen / Inhalte
This lecture teaches basic image processing and data analysis. During the practica we will work on programing projects that implement concepts from the theory lessons.
This is the content:
First the basics of image formation are introduced, and this will give the student a sound basis for learning about such things as:
- Nyquist sample criterion
- Linear and non-linear filters
- Background removal
- Noise statistics
Then an extensive introduction to tomography follows:
- Radon transform
- Algebraic reconstruction techniques
- Reconstruction artifacts
Then it is time for processing of hyperspectral data, with a focus on principal component analysis.
Time permitting, there'll be an introduction to statistical experimental design towards the end.