Half of the course is concerned with image syntesis (computer graphics) and half of the course is on image analysis (image processing).
graphical primitives, rasterization, anti-aliasing, clipping, geometric transformations, viewing transformations, hierarchical scene modelling, culling and hidden surface elimination, colour representation, illumination models and algorithms. C/C++ OpenGL labs.
introduction to and examples of image processing and simple image analysis applications. Intro to deep learning based image interpretation and understanding (fully-connected neural networks and CNNs). Filtering and image enhancement in both the spatial domain as well as in the frequency / Fourier domain. Various image segmentation methods and mathematical morphology. Labs with assignments and Python (alternatively MATLAB).
The candidate will acquire knowledge of basic image synthesis and image analysis principles and algorithms.
The candidate will acquire skills in graphics and image processing programming with commonly used tools.
The candidate will gain competence in realising the potential of basic graphics and image processing techniques, an overview of visual computing, the ability to construct sizeable visual computing applications as well as to absorb further visual computing knowledge.