Course goals
- Knowledge of:
- Ways of describing images, including histograms
- Linear/non-linear filters and morphological filters
- Image processing tasks including edge and curve detection and automatic thresholding
- Color spaces and their relations
- Shape descriptors, and their comparison
- Experience with:
- Designing and deriving filters to enhance images or extract features
- Applying filters, including morphological filters
- Applying the Hough transform to find parameterized structures
- Designing image processing pipelined
Assessment
- Assignments (4) in pairs (mandatory)
- Online exam (mandatory)
The grade of the final (E) amounts to 40% of the final grade. The four assignments count for 10%, 15%, 15% and 20% of the final grade, respectively.
You are eligible for the re-take exam if your grade on the first exam is at least 4.
There is a strong mathematical component in this course. Required knowledge includes elementary knowledge of finite series, elementary statistics and linear algebra (matrices). If you lack this knowledge, you will be required to master it on your own during the course. For the assignments, experience with C# is recommended.
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Image Processing provides basic knowledge and skills for the analysis and processing of digital images. We discuss fundamental and core techniques such as filters, edges and colors. We also treat advanced topics including corner detection, automatic thresholding, geometric operations and scale-invariant feature transforms. The course will be in English. The assignments are mandatory and require C# skills.
Up-to-date information will appear on the Blackboard page of the course.
Form
- Online lectures with knowledge clips
- Online assignment help sessions
Literature
"Principles of Digital Image Processing" by Burger & Burge. It consists of three volumes, which can be downloaded from SpingerLink (free of charge if you access the pages through the UU domain):
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