Image Processing

In this course we will extend the theory of 1D signal processing to 2D digital signals: images.

We will discuss image properties such as pixels, resolution, histograms, 2D spatial-invariant systems (2D-convolution filters), image enhancement, color spaces en filtering, morphology, 2D Fourier transformations, Hough transform and patch descriptors.

This course will also provide students with the experience of implementing a real-life image processing system and dealing with the challenges entailed by researching different approaches and finding a creative solution for these issues.

• Final written exam (50% of course grade): a written exam scheduled at the end of the quarter is offered covering en testing the acquired knowledge level of the topics discussed in the lectures.

A grade above or equal 5.0 is required to pass the course en for this grade element to contribute to the total course grade.

• Hands-on Python Lab assignments (20% of course grade): Pass / Fail weekly practical lab assignments. They must be submitted for approval within the weekly deadlines. Students can solve their assignments at home (using an own computer) or during the lab sessions.

A grade of or above equal 5.0 on the hands-on assignments (passing ~50% of the lab assignments) is a prerequisite for entering the final exam or the resit exam en for this grade element to contribute to the total course grade. It means the student needs to pass 4 out of 7 lab assignments to enter the final exam or the resit exam. For each passing assignment, the student gains 20% / 7 (around 2.86%) of the course grade.

• Project design and analysis (30% of course grade): A quarter-long Python-based project must be submitted for approval within the indicated deadlines. Students can solve their assignments at home (using their own computer) and during the project sessions.

A grade above or equal to 5.0 is required to pass the course for this grade element to contribute to the total course grade.

Resit

• Resit exam (50% of course grade): covering all materials of the plenary lectures, working groups and Python assignments, is scheduled at the end of the second quarter.

• Repair Lab assignments (20% of course grade): students who have a grade between 4 and 5 can redo one of the lab assignments (choice by the lecturers).

• Repair project (30% of course grade): an extra opportunity to submit the project is offered in Q3 with the limitation of a max grade of a 6.0 for passing the project requirements.

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4)