Embedded Systems

We expect solid knowledge in the following disciplines

• Digital design and computer organization
• Operating systems
• Computer networks
• Programming

Visual Computing

We expect solid knowledge in the following disciplines

• Linear Algebra and Vector Calculus
• Programming in C++ and Python
• Computer Graphics (in theory and programming practice)
• Image Processing (in theory and programming practice)

Regarding linear algebra and programming, we refer to standard books and online material, such as

1. Gilbert Strang: "Introduction to Linear Algebra", Wellesley-Cambridge Press, 2016
2. "C++ Tutorial" on tutorialspoint.com
3. A sample lecture on Bachelor level at the Univeristy of  Siegen: Introduction to Programming

Computer Graphics

Regarding computer graphics, we expect knowledge related to the following sub-topics, that can be found in the literature and online material listed below.

1. Transformations in 2D and 3D
2. Basic concepts in lighting and shading
3. Textures
4. Basic concept of the graphics pipeline
5. Graphics algorithms: Clipping, rasterization
6. Graphics programming experience, e.g. OpenGL with GLSL

Reference material (this list is far from being complete, there is far more material that can be used)

1. edX Online Course "Computer Graphics" from Ravi Ramamoorthi, UC San Diego [covers topics: 1, 2, 4, 6]
2. Steve Marschner, Peter Shirley: "Fundamentals of Computer Graphics", 2015, A K Peters/CRC Press [covers all aspects]
3. Richard S. Wright, Nicholas Haemel, Graham Sellers, Benjamin Lipchak: "OpenGL Superbible: Comprehensive Tutorial and Reference", Addison, Wesely, 2015 [covers all aspects]
4. A sample introductory lecture on Master level at the Univeristy of  Siegen: Scientific Visualization.

Image Processing

Regarding image processing, we expect knowledge related to the following sub-topics, that can be found in the literature and online material listed below.

1. Digital image representation
2. Intensity transformations and spatial filtering
3. Filtering the frequency domain
4. Morphological image processing
5. Segmentation and edge detection

Reference material (this list is far from being complete, there is far more material that can be used)

1. Bernd Girod: Digital Image Processing, Stanford University course material.
2. Rich Radke: Digital Image Processing, Youtube video lecture
3. William K. Pratt, Digital Image Processing, PICS Scientific Inside, 2007
4. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson, 2018
5. A sample introductory lecture on Master level at the Univeristy of  Siegen: Deep Learning