Visual Computing Fundamentals

Half of the course is concerned with image syntesis (computer graphics) and half of the course is on image analysis (image processing). Graphics: graphical primitives, rasterization, anti-aliasing, clipping, geometric transformations, viewing transformations, hierarchical scene modelling, culling and hidden surface elimination, colour representation, illumination models and algorithms. OpenGL labs based on C/C++ or Rust. Image processing: 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. Lab with practical exercises in Python. For more information see also: https://www.idi.ntnu.no/grupper/vis/teaching/ as well as Canvas.

Knowledge: The candidate will acquire knowledge of basic image synthesis and image analysis principles and algorithms. Skills: The candidate will acquire skills in graphics and image processing programming with commonly used tools. General competence: 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.

Lectures and exercises.

Coding exercises and the associated documentation is to be done by the students themselves (no AI tools).

Lectures and examination will be held in English.

Students are free to choose Norwegian or English for their responses in written assessments.