Vak: Computer Vision and Image Processing credits: 4

The acquired skills are relevant ingredients for a general workflow of creating meaningful information out of digital images. In this study, the student will learn how images are represented and stored in computers, implement methods to improve the quality or change characteristics of digital images, apply methods to detect features and segment images automatically. The final goal is for the student to understand and apply simple methods to recognize objects in an image.

Globally, the following subjects will be addressed during the lectures in this study unit:

- Digital representation of gray-scale and color images, including different types of color models (e.g. RGB, HSV);

- Image enhancement using pixel operations (e.g. histogram equalization);

- Image enhancement using spatial and frequency filtering;

- Edge finding methods (Laplace, Sobel);

- Image segmentation techniques;

- Template matching;

- Other methods of object recognition.

There are both practical and theoretical assignments to assess all Learning Outcomes. The practical assignments will be programing assignments (PA) in which students must implement the methods and techniques in question. The theoretical assignment will be a written exam (WE) to test student understanding of the topics. The final grade will be composed by an weighted average of both grades: 0.6*WE + 0.4*PA