Students enrolled in IMA tracks cannot take this course.
PREREQUISITES:
Basic knowledge in Signal Processing
Basic knowledge in Applied Mathematics (differential calculus, probability)
Basic knowledge of Python programming.
OBJECTIVES:
This course is an introduction to digital image processing covering both fundamental
concepts and algorithmic topics.
The course will be composed of a series of lectures and practical sessions, organized to guide
the students towards a good understanding of both theoretical concepts and practical
implementation of the presented methods. During the week, small team projects will give
the students another opportunity to discuss, practice and develop skills in image processing.
Practical sessions will consist of Jupyter notebooks that should be filled with Python code,
and will take place in a room equipped with workstations.
Theoretical lectures represent about half of the course, the other half being reserved to the practical works.
PROGRAMME TO BE FOLLOWED:The lectures will tackle the following topics:
- image representation, Fourier transform, filtering
- radiometry, colorimetry
- image restoration
- image segmentation
- introduction to machine learning for imaging
- introduction to medical imaging
- introduction to computer graphics
